Park, K., Cho, M., Song*, M., Yoo*, S., Baek, H., Kim, S., & Kim, K. (2023). Exploring the potential of OMOP common data model for process mining in healthcare. PLOS ONE, 18(1), 1–24.
@article{park_ploseone_2023,
doi = {10.1371/journal.pone.0279641},
author = {Park, Kangah and Cho, Minsu and Song*, Minseok and Yoo*, Sooyoung and Baek, Hyunyoung and Kim, Seok and Kim, Kidong},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Exploring the potential of OMOP common data model for process mining in healthcare},
year = {2023},
month = jan,
volume = {18},
pages = {1-24},
number = {1},
file = {c_park_plosone_2023.pdf},
impact_factor = {2.776},
sci = {SCIE}
}
Background and objective Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. Methods We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. Results Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM’s usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. Conclusion To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.
Park, G., & Song*, M. (2023). Optimizing Resource Allocation Based on Predictive Process Monitoring. IEEE ACCESS.
@article{park_ieeeaccess_2023,
doi = {10.1109/ACCESS.2023.3267538},
author = {Park, Gyunam and Song*, Minseok},
journal = {IEEE ACCESS},
publisher = {IEEE},
title = {Optimizing Resource Allocation Based on Predictive Process Monitoring},
year = {2023},
sci = {SCIE}
}
Park, K., Lim, J., Hwang, W., Park, J., Song*, M., Kim, B.-I., Park, J. G., & Choi, D. J. (2022). Ridesourcing in manufacturing sites: a framework and case study. International Journal of Industrial Engineering: Theory, Applications and Practice, 29(5).
@article{Park_ijie_2022,
title = {Ridesourcing in manufacturing sites: a framework and case study},
journal = {International Journal of Industrial Engineering: Theory, Applications and Practice},
author = {Park, Kangah and Lim, Jungeun and Hwang, Woohyun and Park, Junhyun and Song*, Minseok and Kim, Byung-In and Park, Jung Goo and Choi, Doo Jin},
year = {2022},
month = oct,
volume = {29},
number = {5},
doi = {10.23055/ijietap.2022.29.5.8325},
file = {c_park_ijie2022.pdf},
impact_factor = {0.633},
sci = {SCIE}
}
With the recent innovations in transportation, ridesourcing services have been proliferating in many countries. There are increasing attempts to apply ridesourcing in the corporate context. Manufacturing companies now install the Industrial Internet of Things (IIOT) sensors to vehicles to obtain real-time data on the movement of goods and materials. Despite the massive amount of data accumulated, little attention has been paid to exploiting the data for vehicle fleet management (FM). This paper proposes an analytical framework to solve two FM problems: how to group organizational units for vehicle sharing and where to deploy the groups. The framework is then validated with a case study of a Korean shipbuilder. The results indicate that grouping departments with similar spatial patterns can reduce the current fleet.
Lim, J., Kim, K., Song*, M., Yoo*, S., Baek, H., Kim, S., Park, S., & Jeong, W.-J. (2022). Assessment of the feasibility of developing a clinical pathway using a clinical order log. Journal of Biomedical Informatics, 128, 104038.
@article{LIM2022104038,
title = {Assessment of the feasibility of developing a clinical pathway using a clinical order log},
journal = {Journal of Biomedical Informatics},
volume = {128},
pages = {104038},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.104038},
author = {Lim, Jungeun and Kim, Kidong and Song*, Minseok and Yoo*, Sooyoung and Baek, Hyunyoung and Kim, Seok and Park, Somin and Jeong, Woo-Jin},
keywords = {Process mining, clinical pathway, feasibility assessment, clinical order log, visualization},
file = {c_lim_jbi2022.pdf},
impact_factor = {6.317},
sci = {SCIE}
}
A clinical pathway (CP) is a tool for effectively managing a care process. There are several research efforts on developing clinical pathways (CPs) in the process mining domain. However, the nature of the data affects data analysis results, and patient clinical variability makes it challenging to develop CPs. Thus, it is crucial to determine candidate care processes that can be standardized as CPs before applying process mining techniques. This paper proposed a method for assessing CP feasibility regarding clinical complexity using clinical order logs from electronic health records. The proposed method consists of data preparation, activity & trace homogeneity evaluations, and process inspection using process mining. Each step consists of metrics to measure the homogeneity of processes and a visualization method to demonstrate the diversity of processes based on the log. The case study was conducted with five surgical groups of patients from a tertiary hospital in South Korea to validate the proposed method. The five groups of patients were successfully assessed. In addition, the visualization methods helped clinical experts grasp the diversity of care processes.
Ryu, D.-H., Kim*, K.-J., Ko, Y. M., Kim, Y.-J., & Song, M. (2022). Collection and Analysis of Electricity Consumption Data in POSTECH Campus (포스텍 캠퍼스의 전력 사용 데이터 수집 및 분석). Journal of Korean Society for Quality Management(품질경영학회지), 50(3), 617–634.
@article{park2022,
author = {Ryu, Do-Hyeon and Kim*, Kwang-Jae and Ko, YoungMyoung and Kim, Young-Jin and Song, Minseok},
title = {Collection and Analysis of Electricity Consumption Data in POSTECH Campus (포스텍 캠퍼스의 전력 사용 데이터 수집 및 분석)},
journal = {Journal of Korean Society for Quality Management(품질경영학회지)},
volume = {50},
number = {3},
pages = {617--634},
year = {2022},
doi = {10.7469/JKSQM.2022.50.3.617},
file = {ryu_jksqm-50-3-617_2022.pdf}
}
Purpose: This paper introduces Pohang University of Science Technology (POSTECH) advanced metering infrastructure (AMI) and Open Innovation Big Data Center (OIBC) platform and analysis results of electricity consumption data collected via the AMI in POSTECH campus.
Methods: We installed 248 sensors in seven buildings at POSTECH for the AMI and collected electricity consumption data from the buildings. To identify the amounts and trends of electricity consumption of the seven buildings, electricity consumption data collected from March to June 2019 were analyzed. In addition, this study compared the differences between the amounts and trends of electricity consumption of the seven buildings before and after the COVID-19 outbreak by using electricity consumption data collected from March to June 2019 and 2020.
Results: Users can monitor, visualize, and download electricity consumption data collected via the AMI on the OIBC platform. The analysis results show that the seven buildings consume different amounts of electricity and have different consumption trends. In addition, the amounts of most buildings were significantly reduced after the COVID-19 outbreak.
Conclusion: POSTECH AMI and OIBC platform can be a good reference for other universities that prepare their own microgrid. The analysis results provides a proof that POSTECH needs to establish customized strategies on reducing electricity for each building. Such results would be useful for energy-efficient operation and preparation of unusual energy consumptions due to unexpected situations like the COVID-19 pandemic.
Ryu, H., Kim, B.-I., Song, M., Kim, H., Lee, D., Lee, S., Shin, J., Yoo, Y.-D., Kim, S. H., & Lee, H. (2022). Optimization of Hydrogen Refueling Stations Deployment and Supply Chain Networks: Current Status and Research Suggestions (수소자동차 충전소 및 공급망 배치 최적화: 현황 및 연구 제언). Journal of the Korean Institute of Industrial Engineers (대한산업공학회지), 48(2), 211–226.
@article{ryu2022,
author = {Ryu, Hyunyoung and Kim, Byung-In and Song, Minseok and Kim, Hyunjoon and Lee, Deoksang and Lee, Seungyeop and Shin, Jaemin and Yoo, Young-Don and Kim, Su Hyun and Lee, Hyejin},
title = {Optimization of Hydrogen Refueling Stations Deployment and Supply Chain Networks: Current Status and Research Suggestions (수소자동차 충전소 및 공급망 배치 최적화: 현황 및 연구 제언)},
journal = {Journal of the Korean Institute of Industrial Engineers (대한산업공학회지)},
volume = {48},
number = {2},
pages = {211--226},
year = {2022},
doi = {10.7232/JKIIE.2022.48.2.211},
file = {ryu_iems_2022.pdf}
}
Hydrogen infrastructure consisting of hydrogen refueling stations and supply chain network is critical in the hydrogen mobility economy. This paper overviews the important key concepts of the hydrogen mobility economy and investigates the status of hydrogen vehicles and refueling stations in Korea and other countries. It also reviews the methodologies for hydrogen station and supply chain network optimization and suggests research agenda for the hydrogen mobility economy.
Cho, H., Ryu, H., & Song*, M. (2022). Pass2vec: Analyzing soccer players’ passing style using deep learning. International Journal of Sports Science & Coaching, 17(2), 355–365.
@article{doi:10.1177/17479541211033078,
author = {Cho, Hyeonah and Ryu, Hyunyoung and Song*, Minseok},
title = {Pass2vec: Analyzing soccer players’ passing style using deep learning},
journal = {International Journal of Sports Science \& Coaching},
volume = {17},
number = {2},
pages = {355-365},
year = {2022},
doi = {10.1177/17479541211033078},
eprint = {https://doi.org/10.1177/17479541211033078},
file = {c_r_cho_ijssc_2022.pdf},
impact_factor = {2.029},
sci = {SSCI}
}
The aim of this research was to analyze the player’s pass style with enhanced accuracy using the deep learning technique. We proposed Pass2vec, a passing style descriptor that can characterize each player’s passing style by combining detailed information on passes. Pass data was extracted from the ball event data from five European football leagues in the 2017–2018 season, which was divided into training and test set. The information on location, length, and direction of passes was combined using Convolutional Autoencoder. As a result, pass vectors were generated for each player. We verified the method with the player retrieval task, which successfully retrieved 76.5% of all players in the top-20 with the descriptor and the result outperformed previous methods. Also, player similarity analysis confirmed the resemblance of players passes on three representative cases, showing the actual application and practical use of the method. The results prove that this novel method for characterizing player’s styles with improved accuracy will enable us to understand passing better for player training and recruitment.
Munoz-Gama, J., Martin, N., Fernandez-Llatas, C., Johnson, O. A., Sepúlveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martinez-Millana, A., Aloini, D., Amantea, I. A., Andrews, R., Arias, M., Beerepoot, I., Benevento, E., Burattin, A., Capurro, D., Carmona, J., Comuzzi, M., … Zerbato, F. (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics, 127, 103994.
@article{MUNOZGAMA2022103994,
title = {Process mining for healthcare: Characteristics and challenges},
journal = {Journal of Biomedical Informatics},
volume = {127},
pages = {103994},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.103994},
author = {Munoz-Gama, Jorge and Martin, Niels and Fernandez-Llatas, Carlos and Johnson, Owen A. and Sepúlveda, Marcos and Helm, Emmanuel and Galvez-Yanjari, Victor and Rojas, Eric and Martinez-Millana, Antonio and Aloini, Davide and Amantea, Ilaria Angela and Andrews, Robert and Arias, Michael and Beerepoot, Iris and Benevento, Elisabetta and Burattin, Andrea and Capurro, Daniel and Carmona, Josep and Comuzzi, Marco and Dalmas, Benjamin and {de la Fuente}, Rene and {Di Francescomarino}, Chiara and {Di Ciccio}, Claudio and Gatta, Roberto and Ghidini, Chiara and Gonzalez-Lopez, Fernanda and Ibanez-Sanchez, Gema and Klasky, Hilda B. and {Prima Kurniati}, Angelina and Lu, Xixi and Mannhardt, Felix and Mans, Ronny and Marcos, Mar and {Medeiros de Carvalho}, Renata and Pegoraro, Marco and Poon, Simon K. and Pufahl, Luise and Reijers, Hajo A. and Remy, Simon and Rinderle-Ma, Stefanie and Sacchi, Lucia and Seoane, Fernando and Song, Minseok and Stefanini, Alessandro and Sulis, Emilio and {ter Hofstede}, Arthur H.M. and Toussaint, Pieter J. and Traver, Vicente and Valero-Ramon, Zoe and van de Weerd, Inge and {van der Aalst}, Wil M.P. and Vanwersch, Rob and Weske, Mathias and Wynn, Moe Thandar and Zerbato, Francesca},
keywords = {Process mining, Healthcare},
file = {munozgama_jbi2022.pdf},
impact_factor = {6.317},
sci = {SCIE}
}
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
Beverungen*, D., Buijs, J. C. A. M., Becker, J., Ciccio, C. D., van der Aalst, W. M. P., Bartelheimer, C., vom Brocke, J., Comuzzi, M., Kraume, K., Leopold, H., Matzner, M., Mendling, J., Ogonek, N., Post, T., Resinas, M., Revoredo, K., del-Río-Ortega, A., Rosa, M. L., Santoro, F. M., … Wolf, V. (2021). Seven Paradoxes of Business Process Management in a Hyper-Connected World. Business & Information Systems Engineering, 63, 145–156.
@article{Beverungen2021SevenPO,
title = {Seven Paradoxes of Business Process Management in a Hyper-Connected World},
author = {Beverungen*, Daniel and Buijs, Joos C. A. M. and Becker, J{\"o}rg and Ciccio, Claudio Di and {van der Aalst}, Wil M.P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and Matzner, Martin and Mendling, Jan and Ogonek, Nadine and Post, Till and Resinas, Manuel and Revoredo, Kate and del-R{\'i}o-Ortega, Adela and Rosa, Marcello La and Santoro, Fl{\'a}via Maria and Solti, Andreas and Song, Minseok and Stein, Armin and Stierle, Matthias and Wolf, Verena},
journal = {Business \& Information Systems Engineering},
year = {2021},
volume = {63},
pages = {145-156},
doi = {10.1007/s12599-020-00646-z},
file = {beverungen_bise_2021.pdf},
impact_factor = {5.837},
sci = {SCIE}
}
Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.
Cho, M., Park, G., Song*, M., Lee, J., Lee, B., & Kum, E. (2021). Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 34(1), 17–24.
@article{9298776,
author = {Cho, Minsu and Park, Gyunam and Song*, Minseok and Lee, Jinyoun and Lee, Byeongeon and Kum, Euiseok},
journal = {IEEE Transactions on Semiconductor Manufacturing},
title = {Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing},
year = {2021},
volume = {34},
number = {1},
pages = {17-24},
doi = {10.1109/TSM.2020.3045686},
file = {c_cho_ieees2021.pdf},
impact_factor = {1.977},
sci = {SCIE}
}
In semiconductor manufacturing, data-driven methodologies have enabled the resolution of various issues, particularly yield management and enhancement. Yield, one of the crucial key performance indicators in semiconductor manufacturing, is mostly affected by production resources, i.e., equipment involved in the process. There is a lot of research on finding the correlation between yield and the status of resources. However, in general, multiple resources are engaged in production processes, which may cause multicollinearity among resources. Therefore, it is important to discover resource paths that are positively or negatively associated with yield. This article proposes a systematic methodology for discovering a resource-oriented transition system model in a semiconductor manufacturing process to identify resource paths resulting in high and low yield. The proposed method is based on the model-based analysis (i.e., finite state machine mining) in process mining and statistical analyses. We conducted an empirical study with real-life data from one of the leading semiconductor manufacturing companies to validate the proposed approach.
Cho, M., Park, G., Song*, M., Lee, J., & Kum, E. (2021). Quality-Aware Resource Model Discovery. Applied Sciences, 11(12), 5730.
@article{app111257301,
author = {Cho, Minsu and Park, Gyunam and Song*, Minseok and Lee, Jinyoun and Kum, Euiseok},
journal = {Applied Sciences},
title = {Quality-Aware Resource Model Discovery},
year = {2021},
volume = {11},
number = {12},
pages = {5730},
doi = {10.3390/app11125730},
impact_factor = {2.679},
file = {c_cho_as_2021.pdf},
sci = {SCIE}
}
Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.
Lee, D., & Song*, M. (2021). MEXchange: A Privacy-preserving Blockchain-based Framework for Health Information Exchange using Ring Signature and Stealth Address. IEEE Access, 9, 158122–158139.
@article{deoksang2021,
author = {Lee, Deoksang and Song*, Minseok},
title = {MEXchange: A Privacy-preserving Blockchain-based Framework for Health Information Exchange using Ring Signature and Stealth Address},
journal = {IEEE Access},
volume = {9},
pages = {158122-158139},
year = {2021},
doi = {10.1109/ACCESS.2021.3130552},
file = {c_lee_ieeea_2021.pdf},
impact_factor = {3.367},
sci = {SCIE}
}
Health information exchange (HIE) refers to the integrated management and secure sharing of health information among healthcare entities. HIE improves healthcare quality and streamlines healthcare administrative work. These advantages have propelled health- care stakeholders to implement HIE. However, challenged by issues such as security, privacy, and costs, HIE is not widespread. Recent studies have suggested blockchain-based HIE for solving security and privacy issues. Unfortunately, existing blockchain-based HIE studies do not consider the privacy issues caused by analyzing senders and receivers of transactions in the blockchain. In this work, we suggest MEXchange, a novel blockchain-based privacy-preserving HIE that prevents the privacy issue by obscuring the sender and concealing receiver addresses. We propose smart contracts and workflow that use ring signature and stealth address for blockchain-based HIE. Software components and implementation of MEXchange on the Ethereum private network are discussed. We evaluate MEXchange quantitatively by measuring the transaction latency and throughput of exchanging. Also, we evaluate MEXchange qualitatively using the requirements of the Office of National Coordinator for Health Information Technology (ONC). Moreover, we proceed with threat modeling based on STRIDE. Finally, we compare MEXchange with Ancile, FHIRChain, Integrating the Healthcare Enterprise Cross-Enterprise Document Sharing (IHE XDS), and MedRec. The MEXchange lowers barriers to the application of blockchain-based HIE systems by mitigating privacy and security issues among healthcare stakeholders.
Ryu, D.-H., Kim, R.-H., Choi, S.-H., Kim*, K.-J., Ko, Y. M., Kim, Y.-J., Song, M., & Choi, D. G. (2020). Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building. Sustainability, 12(20), 8704.
@article{su12208704_2,
title = {Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building},
author = {Ryu, Do-Hyeon and Kim, Ryu-Hee and Choi, Seung-Hyun and Kim*, Kwang-Jae and Ko, Young Myoung and Kim, Young-Jin and Song, Minseok and Choi, Dong Gu},
journal = {Sustainability},
year = {2020},
volume = {12},
number = {20},
pages = {8704},
file = {ryu_sustainability_2020.pdf},
doi = {10.3390/su12208704}
}
Real-time collection of household electricity consumption data has been facilitated by advanced metering infrastructure. In recent studies, collected data have been processed to provide information on household appliance usage. The noise caused by electrical appliances from neighboring households constitutes a major issue, which is related to discomfort and even mental diseases. The assessment of noise discomfort using electricity consumption data has not been dealt with in the literature up to this day. In this study, a method, which utilizes electricity consumption data for the assessment of noise discomfort levels caused by electrical appliances between neighboring households, is proposed. This method is based on the differences in the usage time of electrical appliances in a collective residential building. The proposed method includes the following four steps: data collection and preprocessing, residential units clustering, noise discomfort modeling, and evaluation of noise discomfort. This method is demonstrated through a case study of a campus apartment building. Variations in the noise discomfort assessment model and measures for alleviating noise discomfort are also discussed. The proposed method can guide the application of electricity consumption data to assessment and alleviation of noise discomfort from home appliances at an apartment building.
Park, S.-N., & Song*, M. (2020). A process perspective event-log analysis method for airport BHS (Baggage Handling System)(공항 수하물 처리 시스템 이벤트 로그의 프로세스 관점 분석 방안 연구). The Korean Journal of BigData, 5(1), 181–188.
@article{park2020,
author = {Park, Shin-Nyum and Song*, Minseok},
title = {A process perspective event-log analysis method for airport BHS (Baggage Handling System)(공항 수하물 처리 시스템 이벤트 로그의 프로세스 관점 분석 방안 연구)},
journal = {The Korean Journal of BigData},
volume = {5},
number = {1},
pages = {181--188},
year = {2020},
doi = {10.36498/kbigdt.2020.5.1.181},
file = {c_park_koreaBD_2020.pdf}
}
급증하는 항공 여객의 성장세에 맞춰 여객 터미널의 규모가 대형화됨에 따라 출발, 도착, 환승 여객들이 소지한 수하물을 최단 시간 내에 신속, 정확하게 처리할 수 있는 다양한 데이터 기술들이 접목된 첨단 수하물 처리시스템(Baggage Handling System; 이하 BHS)이 필수 요소가 되었다. 따라서 본 연구에서는 공항 수하물 처리시스템 운영의 고도화를 위해, 프로세스 관점의 데이터 분석 방법론을 통한 국내 공항의 수하물 처리능력 분석 방법을 소개하고, 이벤트 로그 기반 주요 지점의 정확한 부하예측 방법을 제시하여, 자원의 선제적 배치 및 flight-carrocel 스케줄링 최적화 문제 등 향후 첨단화된 BHS 운영 전략으로 이어질 수 있도록 한다. 분석에 사용된 데이터는 공공데이터 포털에서 얻을 수 있는 ‘전국 공항 수송실적’, ‘항공기 운항 정보’ API를 활용하였다. 국내 공항 BHS 시뮬레이션 모델에 해당 방법을 적용한 결과, 높은 수준의 예측성능을 확인 할 수 있었다.
Park, G., & Song*, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129, 113191.
@article{PARK2020113191,
title = {Predicting performances in business processes using deep neural networks},
journal = {Decision Support Systems},
volume = {129},
pages = {113191},
year = {2020},
issn = {0167-9236},
doi = {https://doi.org/10.1016/j.dss.2019.113191},
author = {Park, Gyunam and Song*, Minseok},
keywords = {Process mining, Process management, Online operational support, Process performance prediction, Deep neural networks},
file = {c_r_park_dss_2020.pdf},
impact_factor = {4.721},
sci = {SCIE}
}
Online operational support is gaining increasing interest due to the availability of real-time data and sufficient computing power, such as predictive business process monitoring. Predictive business process monitoring aims at providing timely information that enables proactive and corrective actions to improve process enactments and mitigate risks. There are a handful of research works focusing on the predictions at the instance level. However, it is more practical to predict the performance of processes at the process model level and detect potential weaknesses in the process to facilitate the proactive actions that will improve the process execution. Thus, in this paper, we propose a novel method to predict the future performances of a business process at the process model level. More in detail, we construct an annotated transition system and generate a process representation matrix from it. Based on the process representation matrix, we build performance prediction models using deep neural networks that consider both spatial and temporal dependencies present in the underlying business process. To validate the proposed method, we performed case studies on three real-life logs.
Cho, M., Song*, M., Park, J., Yeom, S.-R., Wang, I.-J., & Choi, B.-K. (2020). Process Mining-Supported Emergency Room Process Performance Indicators. International Journal of Environmental Research and Public Health, 17(17), 6290.
@article{ijerph17176290,
author = {Cho, Minsu and Song*, Minseok and Park, Junhyun and Yeom, Seok-Ran and Wang, Il-Jae and Choi, Byung-Kwan},
title = {Process Mining-Supported Emergency Room Process Performance Indicators},
journal = {International Journal of Environmental Research and Public Health},
volume = {17},
year = {2020},
number = {17},
issn = {1660-4601},
doi = {10.3390/ijerph17176290},
pages = {6290},
impact_factor = {2.849},
file = {c_cho_ijerph-17-06290-v2.pdf},
sci = {SCIE/SSCI}
}
Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil’s quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea to validate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes’ performance.
Cho, M., Kim, K., Lim, J., Baek, H., Kim, S., Hwang, H., Song*, M., & Yoo, S. (2020). Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears. International Journal of Medical Informatics, 133, 104015.
@article{CHO2020104015,
title = {Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears},
journal = {International Journal of Medical Informatics},
volume = {133},
pages = {104015},
year = {2020},
issn = {1386-5056},
doi = {https://doi.org/10.1016/j.ijmedinf.2019.104015},
author = {Cho, Minsu and Kim, Kidong and Lim, Jungeun and Baek, Hyunyoung and Kim, Seok and Hwang, Hee and Song*, Minseok and Yoo, Sooyoung},
impact_factor = {3.025},
file = {c_r_cho_IJMI_CP_2020.pdf},
sci = {SCIE}
}
Objective
A clinical pathway is one of the tools used to support clinical decision making that provides a standardized care process in a specific context. The objective of this research was to develop a method for building data-driven clinical pathways using electronic health record data.
Materials and methods
We proposed a matching rate-based clinical pathway mining algorithm that produces the optimal set of clinical orders for each clinical stage by employing matching rates. To validate the approach, we utilized two different datasets of deidentified inpatient records directly related to total laparoscopic hysterectomy (TLH) and rotator cuff tears (RCTs) from a hospital in South Korea. The derived data-driven clinical pathways were evaluated with knowledge-based models by health professionals using a delta analysis.
Results
Two different data-driven clinical pathways, i.e., TLH and RCTs, were produced by applying the matching rate-based clinical pathway mining algorithm. We identified that there were significant differences in clinical orders between the data-driven and knowledge-based models. Additionally, the data-driven clinical pathways based on our algorithm outperformed the models by clinical experts, with average matching rates of 82.02% and 79.66%, respectively.
Conclusion
The proposed algorithm will be helpful for supporting clinical decisions and directly applicable in medical practices.
Obregon, J., Song, M., & Jung, J.-Y. (2019). InfoFlow: Mining Information Flow Based on User Community in Social Networking Services. IEEE Access, 7, 48024–48036.
@article{8681519,
author = {Obregon, Josue and Song, Minseok and Jung, Jae-Yoon},
journal = {IEEE Access},
title = {InfoFlow: Mining Information Flow Based on User Community in Social Networking Services},
year = {2019},
volume = {7},
number = {},
pages = {48024-48036},
doi = {10.1109/ACCESS.2019.2906081},
impact_factor = {3.367},
sci = {SCIE},
file = {obregon_ieeea_2019.pdf}
}
Online social networking services (SNSs) have emerged rapidly and have become huge data sources for social network analysis. The spread of the content generated by users is crucial in SNS, but there is only a handful of research works on information diffusion and, more precisely, information diffusion flow. In this paper, we propose a novel method to discover information diffusion processes from SNS data. The method starts preprocessing the SNS data using a user-centric algorithm of community detection based on modularity maximization with the purpose of reducing the complexity of the noisy data. After that, the InfoFlow miner generates information diffusion flow models among the user communities discovered from the data. The algorithm is an extension of a traditional process discovery technique called the Flexible Heuristics miner, but the visualization ability of the generated process model is improved with a new measure called response weight, which effectively captures and represents the interactions among communities. An experiment with Facebook data was conducted, and information flow among user communities was visualized. Additionally, a quality assessment of the models was carried out to demonstrate the effectiveness of the method. The final constructed models allowed us to identify useful information such as how the information flows between communities and information disseminators and receptors within communities.
Park, K., Kwahk, J., Han, S. H., Song, M., Choi, D. G., Jang, H., Kim, D., Won, Y. D., & Jeong, I. (2019). Modelling the Intrusive Feelings of Advanced Driver Assistance Systems Based on Vehicle Activity Log Data: Case Study for the Lane Keeping Assistance System. International Journal of Automotive Technology, 20, 455–463.
@article{Park2019ModellingTI,
title = {Modelling the Intrusive Feelings of Advanced Driver Assistance Systems Based on Vehicle Activity Log Data: Case Study for the Lane Keeping Assistance System},
author = {Park, Kyudong and Kwahk, Jiyoung and Han, Sung Ho and Song, Minseok and Choi, Dong Gu and Jang, Hyeji and Kim, Dohyeon and Won, Young Deok and Jeong, Insub},
journal = {International Journal of Automotive Technology},
year = {2019},
volume = {20},
pages = {455-463},
impact_factor = {1.245},
sci = {SCIE},
doi = {10.1007/s12239-019-0043-6},
file = {park_ijat2019.pdf}
}
Although the automotive industry has been among the sectors that best-understands the importance of drivers’ affect, the focus of design and research in the automotive field has long emphasized the visceral aspects of exterior and interior design. With the adoption of Advanced Driver Assistance Systems (ADAS), endowing ‘semi-autonomy’ to the vehicles, however, the scope of affective design should be expanded to include the behavioural aspects of the vehicle. In such a ‘shared-control’ system wherein the vehicle can intervene in the human driver’s operations, a certain degree of ‘intrusive feelings’ are unavoidable. For example, when the Lane Keeping Assistance System (LKAS), one of the most popular examples of ADAS, operates the steering wheel in a dangerous situation, the driver may feel interrupted or surprised because of the abrupt torque generated by LKAS. This kind of unpleasant experience can lead to prolonged negative feelings such as irritation, anxiety, and distrust of the system. Therefore, there are increasing needs of investigating the driver’s affective responses towards the vehicle’s dynamic behaviour. In this study, four types of intrusive feelings caused by LKAS were identified to be proposed as a quantitative performance indicator in designing the affectively satisfactory behaviour of LKAS. A metric as well as a statistical data analysis method to quantitatively measure the intrusive feelings through the vehicle sensor log data.
Cho, M., Song*, M., Yoo, S., & Reijers, H. A. (2019). An Evidence-Based Decision Support Framework for Clinician Medical Scheduling. IEEE Access, 7, 15239–15249.
@article{8621008,
author = {Cho, Minsu and Song*, Minseok and Yoo, Sooyoung and Reijers, Hajo A.},
journal = {IEEE Access},
title = {An Evidence-Based Decision Support Framework for Clinician Medical Scheduling},
year = {2019},
volume = {7},
pages = {15239-15249},
doi = {10.1109/ACCESS.2019.2894116},
impact_factor = {3.367},
sci = {SCIE},
file = {c_cho_ieeea_2019.pdf}
}
In healthcare management, waiting time for consultation is an important measure that has strong associations with patient’s satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A typical approach for deriving the optimized schedules is to perform experiments using discrete event simulation. The existing work has developed how to build a simulation model based on process mining techniques. However, applying this method for outpatient processes straightforwardly, in particular medical scheduling, is challenging: 1) the collected data from electronic health record system requires a series of processes to acquire simulation parameters from the raw data; and 2) even if the derived simulation model fully reflects the reality, there is no systematic approach to deriving effective improvements for simulation analysis, i.e., experimental scenarios. To overcome these challenges, this paper proposes a novel decision support framework for a clinician’s schedule using simulation analysis. In the proposed framework, a data-driven simulation model is constructed based on process mining analysis, which includes process discovery, patient arrival rate analysis, and service time analysis. Also, a series of steps to derive the optimal improvement method from the simulation analysis is included in the framework. To demonstrate the usefulness of our approach, we present the case study results with real-world data in a hospital.
Baek, H., Cho, M., Kim, S., Hwang, H., Song*, M., & Yoo, S. (2018). Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PLOS ONE, 13(4), 1–16.
@article{10.1371/journal.pone.0195901,
doi = {10.1371/journal.pone.0195901},
author = {Baek, Hyunyoung and Cho, Minsu and Kim, Seok and Hwang, Hee and Song*, Minseok and Yoo, Sooyoung},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Analysis of length of hospital stay using electronic health records: A statistical and data mining approach},
year = {2018},
month = apr,
volume = {13},
pages = {1--16},
number = {4},
impact_factor = {2.776},
sci = {SCIE},
file = {c_baek_plosone_2018.pdf}
}
Background
The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently.
Materials and methods
Research subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS.
Results
Overall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS.
Conclusions
Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.
Park, G., Kwak*, J., Han, S., Song, M., Choi, D. G., Jang, H., & Kim, D. (2018). Measuring the intrusive feeling of a lane keeping assistance system(차선 유지 보조 시스템으로 인해 발생하는 이질감의 계측에 대한 연구). Journal of the Ergonomics Society of Korea, 37(4), 459–472.
@article{park2017,
author = {Park, Gyudong and Kwak*, Jiyoung and Han, Sungho and Song, Minseok and Choi, Dong Gu and Jang, Hyeji and Kim, Dohyun},
title = {Measuring the intrusive feeling of a lane keeping assistance system(차선 유지 보조 시스템으로 인해 발생하는 이질감의 계측에 대한 연구)},
journal = {Journal of the Ergonomics Society of Korea},
volume = {37},
number = {4},
pages = {459--472},
year = {2018},
doi = {10.5143/JESK.2018.37.4.459},
file = {park_jesk_2018.pdf}
}
Objective: The objective of this study is to investigate the intrusive feeling caused by a Lane Keeping Assistance System (LKAS) and measure it objectively through the sensor log data of a vehicle. Background: LKAS, one of the Advanced Driver Assistance System (ADAS), directly intervenes in the steering wheel control to assist the driver to keep the lane. Due to the torque generated by the LKAS, the driver has a different and undesirable feeling from a usual situation, which can affect the driving experience. Nevertheless, the performance of the LKAS in most studies focused only on how well the system keeps the lane.
Method: Through an actual driving test using a test vehicle equipped with an LKAS, two ADAS experts and two human factor experts observed the intrusive feeling while collecting sensor log data at the same time. After the types of the intrusive feeling were classified, variables that could account for the feeling were derived. Then, this study compared the data collected from the vehicle with and without the intrusive feeling.
Results: Three types of the intrusive feeling were observed: abrupt lateral change, steering wheel vibration, and heavy steering. The lateral speed (LS), the high-pass filtered steering angle (FSA), and the interrupt torque (IT) were then derived as the variables that could explain each feeling, respectively. It is found that the patterns of the derived variables are different in driving with and without the intrusive feeling.
Conclusion and Application: The intrusive feeling found in this study can be used as a performance index in the development stage of the LKAS. It will be helpful to systematically reduce the intrusive feeling.
Cho, M., Song*, M., Comuzzi, M., & Yoo, S. (2017). Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques. Decision Support Systems, 104, 92–103.
@article{CHO2017922,
title = {Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques},
journal = {Decision Support Systems},
volume = {104},
pages = {92-103},
year = {2017},
issn = {0167-9236},
doi = {https://doi.org/10.1016/j.dss.2017.10.004},
author = {Cho, Minsu and Song*, Minseok and Comuzzi, Marco and Yoo, Sooyoung},
impact_factor = {4.721},
sci = {SCIE},
file = {c_r_cho_dss_2017.pdf}
}
The management of business processes in modern times is rapidly shifting towards being evidence-based. Business process evaluation indicators tend to focus on process performance only, neglecting the definition of indicators to evaluate other concerns of interest in different phases of the business process lifecycle. Moreover, they usually do not discuss specifically which data must be collected to calculate indicators and whether collecting these data is feasible or not. This paper proposes a business process assessment framework focused on the process redesign lifecycle phase and tightly coupled with process mining as an operational framework to calculate indicators. The framework includes process performance indicators and indicators to assess whether process redesign best practices have been applied and to what extent. Both sets of indicators can be calculated using standard process mining functionality. This, implicitly, also defines what data must be collected during process execution to enable their calculation. The framework is evaluated through case studies and a thorough comparison against other approaches in the literature.
Cho, M., Kim, D., Song*, M., Kim, G., Jung, C., & Kim, K. (2017). A development on a predictive model for buying unemployment insurance program based on public data(공공데이터 기반 고용보험 가입 예측 모델 개발 연구). Korean Journal of BigData, 2(2), 17–31.
@article{cho2017,
author = {Cho, Minsu and Kim, Dohyun and Song*, Minseok and Kim, Gwangyong and Jung, Chungsik and Kim, Kidae},
title = {A development on a predictive model for buying unemployment insurance program based on public data(공공데이터 기반 고용보험 가입 예측 모델 개발 연구)},
journal = {Korean Journal of BigData},
volume = {2},
number = {2},
pages = {17--31},
year = {2017},
doi = {10.36498/kbigdt.2017.2.2.17},
file = {c_cho_koreaBD_2017}
}
With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.
(빅데이터의 중요성이 증가함에 따라 공공기관에서는 다양한 빅데이터 관련 인프라를 제공하고 있으며, 그 중 하나가 공공데이터이다. 공공데이터 기반의 다양한 활용 사례가 공유되고 있으며, 공공기관에서도 데이터 기반의 모델을 통해 공공의 문제를 해결하려는 움직임을 보이고 있다. 대표적으로 사회 보험 중 하나인 고용보험 케이스가 있다. 고용보험은 근로자의 권익 보호를 위해 근로자를 고용한 모든 사업주가 필수적으로 가입하여야 하는 보험이지만 가입누락의 경우가 많다. 가입누락을 막기 위한 데이터 기반의 접근이 필요하지만, 분산된 형태의 공공데이터, 수집 시기의 차이로 인해 데이터 통합이 어렵고, 체계적인 방법론이 부재한 상황이다. 본 논문에서는 공공데이터를 기반의 고용보험 가입 예측을 위한 모델 도출 방법론을 제시하고자 한다. 본 방법론은 데이터 수집, 데이터 통합 및 전처리, 데이터 탐색 및 이력 데이터 분석, 예측 모델 도출을 포함하며, 프로세스 마이닝 및 데이터 마이닝을 활용한다. 또한, 사례 연구를 통해 본 방법론의 유효성을 검증한다.)
Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., Hwang, H., & Song*, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34–43.
@article{YOO201634,
title = {Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital},
journal = {International Journal of Medical Informatics},
volume = {88},
pages = {34-43},
year = {2016},
issn = {1386-5056},
doi = {https://doi.org/10.1016/j.ijmedinf.2015.12.018},
author = {Yoo, Sooyoung and Cho, Minsu and Kim, Eunhye and Kim, Seok and Sim, Yerim and Yoo, Donghyun and Hwang, Hee and Song*, Minseok},
impact_factor = {3.745},
sci = {SCIE}
}
Introduction
Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate.
Methods
This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated.
Results
Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost.
Conclusion
This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations.
Lee, Y., Song*, M., Ha, S., Baek, T., & Son, S. (2016). Big data cloud service for manufacturing process analysis. Korean Journal of BigData, 1(1), 41–51.
@article{lee2016a,
author = {Lee, Yonghyuk and Song*, Minseok and Ha, S. and Baek, T. and Son, S.},
title = {Big data cloud service for manufacturing process analysis},
journal = {Korean Journal of BigData},
volume = {1},
number = {1},
pages = {41--51},
year = {2016}
}
Lee, Y., Yi, H., Song*, M., Lee, S., & Park, S. (2016). Process analysis in supply chain management with process mining: A case study. Korean Journal of BigData, 1(2), 65–78.
@article{lee2016,
author = {Lee, Yonghyuk and Yi, Hojung and Song*, Minseok and Lee, S. and Park, S.},
title = {Process analysis in supply chain management with process mining: A case study},
journal = {Korean Journal of BigData},
volume = {1},
number = {2},
pages = {65--78},
year = {2016}
}
Yahya, B. N., Song*, M., Bae, H., Sul, S.-ook, & Wu, J.-Z. (2016). Domain-driven actionable process model discovery. Computers & Industrial Engineering, 99, 382–400.
@article{YAHYA2016382,
title = {Domain-driven actionable process model discovery},
journal = {Computers \& Industrial Engineering},
volume = {99},
pages = {382-400},
year = {2016},
issn = {0360-8352},
doi = {https://doi.org/10.1016/j.cie.2016.05.010},
author = {Yahya, Bernardo Nugroho and Song*, Minseok and Bae, Hyerim and Sul, Sung-ook and Wu, Jei-Zheng},
impact_factor = {2.623},
file = {c_r_yhaya_cie_2016.pdf},
sci = {SCIE}
}
Process discovery is a type of process mining that constructs a process model from the event logs of an information system. The model discovered using process discovery techniques and the process as perceived by users will always differ in some ways and to some extents. In particular, less structured process, such as operational process in business and manufacturing, often result overly confusing, spaghetti-like, process models caused by the inherent complexity of the process. As a result, the mined model has many limitations for providing the users with explicit knowledge that can be directly used to influence behavior for the user’s interest. Explicit knowledge, as later called by actionable knowledge, is an important representation on measuring the interestingness of mined patterns. This actionable knowledge, which is incorporated with users’ background knowledge and based on some notions of actionable rules, can result an actionable process model. Undoubtedly, domain experts, who know the process well, play a key role to enhance the mined model into an actionable model by their involvements during the discovery process. This paper presents a discovery method to obtain an actionable process model that is based on both the event relation in the log and users’ knowledge to improve the incompatibility of the traditional process mining approaches. Users can set their knowledge in terms of constraints. Unlike the existing approach, the proposed approach synthesizes the activity proximity and attempts to extract behavior satisfied by the constraints which may be hidden in the event logs for resulting an actionable process model. In addition, the proposed method is used in order to achieve a sound process model when the existence of the constraints does not satisfy the workflow soundness property. The method was implemented in the ProM framework and tested on a real process.
Cho, M., Song*, M., & Yoo, S. (2015). A systematic methodology for outpatient process analysis based on process mining. International Journal of Industrial Engineering, 22(4), 480–493.
@article{cho_IJIE,
author = {Cho, Minsu and Song*, Minseok and Yoo, Sooyoung},
title = {A systematic methodology for outpatient process analysis based on process mining},
journal = {International Journal of Industrial Engineering},
volume = {22},
number = {4},
year = {2015},
pages = {480--493},
impact_factor = {0.537},
sci = {SCIE}
}
Lee, J., Sung, S., Song, M., & Choi*, I. (2015). A business process simulation framework incorporating the effects of organizational structure. International Journal of Industrial Engineering, 22(4), 454–466.
@article{lee_IJIE,
author = {Lee, Jinyoun and Sung, Sanghyun and Song, Minseok and Choi*, Injun},
title = {A business process simulation framework incorporating the effects of organizational structure},
journal = {International Journal of Industrial Engineering},
volume = {22},
number = {4},
year = {2015},
pages = {454--466},
impact_factor = {0.537},
sci = {SCIE}
}
Yang, H., & Song*, M. (2015). Analyzing repair processes using process mining: A case study. Journal of the Korean Institute of Industrial Engineers, 41(1), 86–96.
@article{yang2015,
author = {Yang, Hanna and Song*, Minseok},
title = {Analyzing repair processes using process mining: A case study},
journal = {Journal of the Korean Institute of Industrial Engineers},
volume = {41},
number = {1},
pages = {86--96},
year = {2015}
}
Yoo*, S., Cho, M., Kim, S., Kim, E., Park, S. M., Kim, K., Hwang, H., & Song, M. (2015). Conformance Analysis of Clinical Pathway Using Electronic Health Record Data. Health Informatics Research, 21(3), 161–166.
@article{doi:10.4258/hir.2015.21.3.161,
author = {Yoo*, Sooyoung and Cho, Minsu and Kim, Seok and Kim, Eunhye and Park, So Min and Kim, Kidong and Hwang, Hee and Song, Minseok},
title = {Conformance Analysis of Clinical Pathway Using Electronic Health Record Data},
journal = {Health Informatics Research},
volume = {21},
number = {3},
pages = {161--166},
year = {2015},
doi = {10.4258/hir.2015.21.3.161},
impact_factor = {0.56},
sci = {ESCI}
}
Sutrisnowati, R. A., Bae*, H., & Song, M. (2015). Bayesian network construction from event log for lateness analysis in port logistics. Computers & Industrial Engineering, 89, 53–66.
@article{SUTRISNOWATI201553,
title = {Bayesian network construction from event log for lateness analysis in port logistics},
journal = {Computers \& Industrial Engineering},
volume = {89},
pages = {53-66},
year = {2015},
issn = {0360-8352},
doi = {https://doi.org/10.1016/j.cie.2014.11.003},
author = {Sutrisnowati, Riska Asriana and Bae*, Hyerim and Song, Minseok},
impact_factor = {2.623},
sci = {SCIE}
}
Kim, E., Kim, S., Song, M., Kim, S., Yoo, D., Hwang, H., & Yoo*, S. (2013). Discovery of out-patient care process of a tertiary university hospital using process mining. Health Informatics Research, 19(1), 42–49.
@article{eunhye2013,
author = {Kim, Eunhye and Kim, Seok and Song, Minseok and Kim, Seongjoo and Yoo, Donghyun and Hwang, Hee and Yoo*, Sooyoung},
title = {Discovery of out-patient care process of a tertiary university hospital using process mining},
journal = {Health Informatics Research},
volume = {19},
number = {1},
pages = {42--49},
year = {2013},
impact_factor = {0.56},
sci = {ESCI}
}
Song*, M., Yang, H., Siadat, S. H., & Pechenizkiy, M. (2013). A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Systems with Applications, 40(9), 3722–3737.
@article{SONG20133722,
title = {A comparative study of dimensionality reduction techniques to enhance trace clustering performances},
journal = {Expert Systems with Applications},
volume = {40},
number = {9},
pages = {3722-3737},
year = {2013},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2012.12.078},
author = {Song*, Minseok and Yang, Hanna and Siadat, Seyed Hossein and Pechenizkiy, Mykola},
impact_factor = {5.452},
sci = {SCIE}
}
Jeon, D., Yahya, B. N., Bae*, H., Song, M., Sul, S.-ook, & Sutrisnowati, R. A. (2013). Conceptual framework for container-handling process analytics. ICIC Express Letters, 7(6), 1919–1924.
@article{Jeon2013,
title = {Conceptual framework for container-handling process analytics},
author = {Jeon, Daeuk and Yahya, Bernardo Nugroho and Bae*, Hyerim and Song, Minseok and Sul, Sung-ook and Sutrisnowati, Riska Asriana},
journal = {ICIC Express Letters},
volume = {7},
number = {6},
pages = {1919--1924},
year = {2013}
}
Siadat, S. H., & Song*, M. (2012). Understanding requirement engineering for context-aware system-based applications. Journal of Software Engineering and Applications, 5(8), 536–544.
@article{Seyed2012,
author = {Siadat, Seyed Hossein and Song*, Minseok},
title = {Understanding requirement engineering for context-aware system-based applications},
journal = {Journal of Software Engineering and Applications},
volume = {5},
number = {8},
pages = {536--544},
year = {2012}
}
Lee, S., Ryu*, K., & Song, M. (2012). Process improvement for PDM/PLM systems by using process mining. Korean CAD/CAM Journal, 17, 294–302.
@article{lee2012,
author = {Lee, S. and Ryu*, Kwangryul and Song, Minseok},
title = {Process improvement for PDM/PLM systems by using process mining},
journal = {Korean CAD/CAM Journal},
volume = {17},
pages = {294--302},
year = {2012}
}
van der Aalst*, W. M. P., Schonenberg, M. H., & Song, M. (2011). Time prediction based on process mining. Information Systems, 36(2), 450–475.
@article{VANDERAALST2011450,
title = {Time prediction based on process mining},
journal = {Information Systems},
volume = {36},
number = {2},
pages = {450-475},
year = {2011},
issn = {0306-4379},
doi = {https://doi.org/10.1016/j.is.2010.09.001},
author = {{van der Aalst}*, Wil M.P. and Schonenberg, M. Helen and Song, Minseok},
keywords = {Process mining, Business process management, Time prediction, Performance analysis, Business intelligence},
impact_factor = {2.466},
sci = {SCI}
}
Koschmider*, A., Song, M., & Reijers, H. A. (2010). Social Software for Business Process Modeling. Journal of Information Technology, 25(3), 308–322.
@article{doi:10.1057/jlt.2009.21,
author = {Koschmider*, Agnes and Song, Minseok and Reijers, Hajo A},
title = {Social Software for Business Process Modeling},
journal = {Journal of Information Technology},
volume = {25},
number = {3},
pages = {308-322},
year = {2010},
doi = {10.1057/jlt.2009.21},
impact_factor = {3.625},
sci = {SSCI}
}
Reijers*, H. A., Song, M., & Jung, B. (2009). Analysis of a Collaborative Workflow Process with Distributed Actors. Information Systems Frontiers, 11, 307–322.
@article{reijers_isf09,
author = {Reijers*, Hajo A. and Song, Minseok and Jung, Byungduk},
volume = {11},
pages = {307-322},
journal = {Information Systems Frontiers},
title = {Analysis of a Collaborative Workflow Process with Distributed Actors},
year = {2009},
impact_factor = {3.63},
sci = {SCIE}
}
Rozinat*, A., Mans, R. S., Song, M., & van der Aalst, W. M. P. (2009). Discovering Simulation Models. Information Systems, 34(3), 305–327.
@article{rozinat2009,
author = {Rozinat*, Anne and Mans, Ronny S. and Song, Minseok and {van der Aalst}, Wil M.P.},
journal = {Information Systems},
title = {{Discovering Simulation Models}},
year = {2009},
volume = {34},
number = {3},
pages = {305--327},
impact_factor = {2.446},
sci = {SCIE}
}
Han*, K. H., Kang, J. G., & Song, M. (2009). Two-stage process analysis using the process-based performance measurement framework and business process simulation. Expert Systems with Applications, 36(3, Part 2), 7080–7086.
@article{HAN20097080,
title = {Two-stage process analysis using the process-based performance measurement framework and business process simulation},
journal = {Expert Systems with Applications},
volume = {36},
number = {3, Part 2},
pages = {7080-7086},
year = {2009},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2008.08.035},
author = {Han*, Kwan Hee and Kang, Jin Gu and Song, Minseok},
impact_factor = {5.452},
sci = {SCIE}
}
Do, N., Choi*, I., & Song, M. (2008). Propagation of engineering changes to multiple product data views using history of product structure changes. International Journal of Computer Integrated Manufacturing, 21(1), 19–32.
@article{doi:10.1080/09511920600786612,
author = {Do, Namchul and Choi*, Injun and Song, Minseok},
title = {Propagation of engineering changes to multiple product data views using history of product structure changes},
journal = {International Journal of Computer Integrated Manufacturing},
volume = {21},
number = {1},
pages = {19-32},
year = {2008},
doi = {10.1080/09511920600786612},
impact_factor = {2.861},
sci = {SCIE}
}
Song*, M., Günther, C. W., van der Aalst, W. M. P., & Jung, J. (2008). Improving process mining with trace clustering. Journal of the Korean Institute of Industrial Engineers, 34(4), 460–469.
@article{song2008,
author = {Song*, Minseok and Günther, C.W. and van der Aalst, W.M.P. and Jung, J.},
title = {Improving process mining with trace clustering},
journal = {Journal of the Korean Institute of Industrial Engineers},
volume = {34},
number = {4},
pages = {460--469},
year = {2008}
}
Song*, M., & van der Aalst, W. M. P. (2008). Towards Comprehensive Support for Organizational Mining
. Decision Support Systems, 46(1), 300–317.
@article{song2009,
author = {Song*, Minseok and {van der Aalst}, Wil M.P.},
date-added = {2009-01-18 14:15:42 +0100},
date-modified = {2009-01-18 14:15:44 +0100},
journal = {Decision Support Systems},
number = {1},
pages = {300--317},
title = {{Towards Comprehensive Support for Organizational Mining}
},
volume = {46},
year = {2008},
impact_factor = {4.721},
sci = {SCIE}
}
Rozinat*, A., Mans, R. S., Song, M., & van der Aalst, W. M. P. (2008). Discovering colored Petri nets from event logs
. International Journal on Software Tools for Technology Transfer Volume, 10(1), 57–74.
@article{song2008stt,
author = {Rozinat*, Anne and Mans, Ronny S. and Song, Minseok and {van der Aalst}, Wil M.P.},
journal = {International Journal on Software Tools for Technology Transfer volume},
number = {1},
pages = {57--74},
title = {{Discovering colored Petri nets from event logs}
},
volume = {10},
year = {2008},
impact_factor = {0.945},
sci = {SCIE}
}
Choi*, I., Song, M., Kim, K., & Lee, Y. (2007). Analysis of social relations among organizational units derived from process models and redesign of organization structure. Journal of the Korean Institute of Industrial Engineers, 33(1), 11–25.
@article{song2007,
author = {Choi*, Injun and Song, Minseok and Kim, Kwangmyung and Lee, Yonghyuk},
title = {Analysis of social relations among organizational units derived from process models and redesign of organization structure},
journal = {Journal of the Korean Institute of Industrial Engineers},
volume = {33},
number = {1},
pages = {11--25},
year = {2007}
}
Aalst*, W. M. P. van der, Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Medeiros, A. K. A. de, Song, M., & Verbeek, H. M. W. (2007). Business Process Mining: An Industrial Application. Information Systems, 32(5), 713–732.
@article{aalrwsis,
author = {Aalst*, {Wil M.P. van der} and Reijers, Hajo A. and Weijters, A.J.M.M. and van Dongen, B.F. and Medeiros, {A.K. Alves de} and Song, Minseok and Verbeek, H.M.W.},
journal = {Information Systems},
number = {5},
pages = {713--732},
title = {Business Process Mining: An Industrial Application},
volume = {32},
year = {2007},
impact_factor = {2.446},
sci = {SCIE}
}
Jung, J., Choi*, I., & Song, M. (2007). An integration architecture for knowledge management systems and business process management systems. Computers in Industry, 58(1), 21–34.
@article{JUNG200721,
title = {An integration architecture for knowledge management systems and business process management systems},
journal = {Computers in Industry},
volume = {58},
number = {1},
pages = {21-34},
year = {2007},
issn = {0166-3615},
doi = {https://doi.org/10.1016/j.compind.2006.03.001},
author = {Jung, Jisoo and Choi*, Injun and Song, Minseok},
impact_factor = {3.945},
sci = {SCIE}
}
Choi*, I., Suh, S.-H., Kim, K., Song, M., Jang, M., & Lee, B.-E. (2006). Development process and data management of TurnSTEP: a STEP-compliant CNC system for turning. International Journal of Computer Integrated Manufacturing, 19(6), 546–558.
@article{doi:10.1080/09511920600622072,
author = {Choi*, Injun and Suh, S. -H. and Kim, K. and Song, Minseok and Jang, M. and Lee, B. -E.},
title = {Development process and data management of TurnSTEP: a STEP-compliant CNC system for turning},
journal = {International Journal of Computer Integrated Manufacturing},
volume = {19},
number = {6},
pages = {546-558},
year = {2006},
publisher = {Taylor & Francis},
doi = {10.1080/09511920600622072},
impact_factor = {2.861},
sci = {SCIE}
}
Jung, J., Song, M., & Choi*, I. (2005). A framework for integration of knowledge management and business process management. IE Interfaces, 18(1), 52–62.
@article{song2005,
author = {Jung, Jisoo and Song, Minseok and Choi*, Injun},
title = {A framework for integration of knowledge management and business process management},
journal = {IE Interfaces},
volume = {18},
number = {1},
pages = {52--62},
year = {2005}
}
Aalst*, W. M. P. van der, Reijers, H. A., & Song, M. (2005). Discovering Social Networks from Event Logs
. Computer Supported Cooperative Work, 14(6), 549–593.
@article{aal_sna_cscw,
author = {Aalst*, Wil {M.P. van der} and Reijers, Hajo A. and Song, Minseok},
date-added = {2009-01-18 14:15:42 +0100},
date-modified = {2009-01-18 14:15:44 +0100},
journal = {Computer Supported Cooperative work},
number = {6},
pages = {549--593},
title = {{Discovering Social Networks from Event Logs}
},
volume = {14},
year = {2005},
impact_factor = {1.825},
sci = {SCIE}
}
Choi*, I., Jeong, H., Song, M., & Ryu, Y. U. (2005). IPM-EPDL: an XML-based executable process definition language. Computers in Industry, 56(1), 85–104.
@article{CHOI200585,
title = {IPM-EPDL: an XML-based executable process definition language},
journal = {Computers in Industry},
volume = {56},
number = {1},
pages = {85-104},
year = {2005},
issn = {0166-3615},
doi = {https://doi.org/10.1016/j.compind.2004.08.011},
author = {Choi*, Injun and Jeong, Hyunbae and Song, Minseok and Ryu, Young U.},
impact_factor = {3.945},
sci = {SCIE}
}
Song*, M., Jung, J., Choi, I., & Cha, J. (2004). The study on a cooperative education system for logistics: the case study of international program in logistics Management systems in Technology University of Eindhoven. Journal of the Korean Society of Supply Chain Management, 4(1), 11–22.
@article{song2004,
author = {Song*, Minseok and Jung, Jisoo and Choi, Injoon and Cha, Jieun},
title = {The study on a cooperative education system for logistics: the case study of international program in logistics Management systems in Technology University of Eindhoven},
journal = {Journal of the Korean Society of Supply Chain Management},
volume = {4},
number = {1},
pages = {11--22},
year = {2004}
}
Choi*, I., Jung, J., & Song, M. (2004). An integrated framework for process knowledge management. International Journal of Innovation and Learning, 1(4), 399–408.
@article{jjung2004,
title = {An integrated framework for process knowledge management},
journal = {International Journal of Innovation and Learning},
volume = {1},
number = {4},
pages = {399-408},
year = {2004},
issn = {0166-3615},
author = {Choi*, Injun and Jung, Jisoo and Song, Minseok},
impact_factor = {1.00},
sci = {ESCI}
}
Choi*, I., Song, M., & Park, C. (2003). Integrated Process Management: A new paradigm for the management of business processes. Journal of Korea Information Science Society, 21(10), 36–44.
@article{song2003,
author = {Choi*, Injun and Song, Minseok and Park, Chulsoon},
title = {Integrated Process Management: A new paradigm for the management of business processes},
journal = {Journal of Korea Information Science Society},
volume = {21},
number = {10},
pages = {36--44},
year = {2003}
}
Choi*, I., Song, M., Park, C., & Park, N. (2003). An XML-based process definition language for integrated process management. Computers in Industry, 50(1), 85–102.
@article{CHOI200385,
title = {An XML-based process definition language for integrated process management},
journal = {Computers in Industry},
volume = {50},
number = {1},
pages = {85-102},
year = {2003},
issn = {0166-3615},
doi = {https://doi.org/10.1016/S0166-3615(02)00139-2},
author = {Choi*, Injun and Song, Minseok and Park, Chulsoon and Park, Namkyu},
impact_factor = {3.945},
sci = {SCIE}
}
Refereed conference proceedings
Jeong, M., Lee, D., & Song, M. (2022). Similar Situation Search using Strategy Videos in Soccer Games (전술분석용 축구 영상을 활용한 유사상황 추출 프레임워크). In Korean Institute of Industrial Engineers (대한산업공학회), Incheon, Korea, November 4-5.
@inproceedings{conf2022_2,
author = {Jeong, M. and Lee, D. and Song, M.},
title = {Similar Situation Search using Strategy Videos in Soccer Games (전술분석용 축구 영상을 활용한 유사상황 추출 프레임워크)},
journal = {In Korean Institute of Industrial Engineers (대한산업공학회), Incheon, Korea, November 4-5},
year = {2022}
}
Jeong, M., Song, M., Kim, K., Yoo, S., Baek, H., & Kim, S. (2022). BPMN based Clinical Pathway Modeling using order log analysis (오더 이벤트 로그 분석을 통한 수술 표준진료지침의 BPMN 모델링 프레임워크). In The Korean Society of Medical Informatics (대한의료정보학회), Jeonju, Korea, November 25 (Best Paper Award (우수연제상)).
@inproceedings{conf2022_1,
author = {Jeong, M. and Song, M. and Kim, K. and Yoo, S. and Baek, H. and Kim, S.},
title = {BPMN based Clinical Pathway Modeling using order log analysis (오더 이벤트 로그 분석을 통한 수술 표준진료지침의 BPMN 모델링 프레임워크)},
journal = {In The Korean Society of Medical Informatics (대한의료정보학회), Jeonju, Korea, November 25 (Best Paper Award (우수연제상))},
year = {2022},
bpa = {Best Paper Award}
}
Park, K., Cho, M., Jang, Y., Chung, S., & Song, M. (2022). Customer journey analysis using process mining and prediction model (프로세스 마이닝과 예측모델을 활용한 고객여정 분석 및 도출). In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1.
@inproceedings{conf2022_4,
author = {Park, K. and Cho, M. and Jang, Y. and Chung, S. and Song, M.},
title = {Customer journey analysis using process mining and prediction model (프로세스 마이닝과 예측모델을 활용한 고객여정 분석 및 도출)},
journal = {In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1},
year = {2022}
}
Park, K., Cho, M., Jang, Y., Chung, S., & Song, M. (2022). Data-driven Customer Journey Analysis using Process Mining and Machine Learning. In Asia Pacific Industrial Engineering and Management Systems Conference(APIEMS), Taichung, Taiwan, November 8.
@inproceedings{conf2022_i1,
author = {Park, K. and Cho, M. and Jang, Y. and Chung, S. and Song, M.},
title = {Data-driven Customer Journey Analysis using Process Mining and Machine Learning},
journal = {In Asia Pacific Industrial Engineering and Management Systems Conference(APIEMS), Taichung, Taiwan, November 8},
year = {2022}
}
Lim, J., Park, K., Kim, K., Yoo, S., Baek, H., Kim, S., & Song, M. (2022). BPMN-based CP modeling considering existing CP models and repetition patterns. In International Conference on Process Mining (ICPM), Bozen-Bolzano, Italy, September 30.
@inproceedings{conf2022_i2,
author = {Lim, J. and Park, K. and Kim, K. and Yoo, S. and Baek, H. and Kim, S. and Song, M.},
title = {BPMN-based CP modeling considering existing CP models and repetition patterns},
journal = {In International Conference on Process Mining (ICPM), Bozen-Bolzano, Italy, September 30},
year = {2022}
}
Shin, J., Lee, D., Song, M., & Kim, J. (2022). Generating a Process Simulation Model in Steelmaking Process using Process Mining. In Advances in Production Management System(APMS), Gyeongju, Korea, September 25.
@inproceedings{conf2022_i3,
author = {Shin, J. and Lee, D. and Song, M. and Kim, J.},
title = {Generating a Process Simulation Model in Steelmaking Process using Process Mining},
journal = {In Advances in Production Management System(APMS), Gyeongju, Korea, September 25},
year = {2022}
}
Lee, D., Ryu, H., Shin, J., Lee, S., Kim, H., Kim, B., Song, M., Yoo, Y., Kim, S., Lee, H., & Hwang, H. (2022). Development of a decision support system (DSS) for optimizing hydrogen vehicle charging stations and supply chain deployment (수소자동차 충전소 및 공급망 배치 최적화를 위한 의사결정지원시스템(DSS) 개발). In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1.
@inproceedings{conf2022_5,
author = {Lee, D. and Ryu, H. and Shin, J. and Lee, S. and Kim, H. and Kim, B. and Song, M. and Yoo, Y. and Kim, S. and Lee, H. and Hwang, H.},
title = {Development of a decision support system (DSS) for optimizing hydrogen vehicle charging stations and supply chain deployment (수소자동차 충전소 및 공급망 배치 최적화를 위한 의사결정지원시스템(DSS) 개발)},
journal = {In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1},
year = {2022}
}
Lee, D., Jang, Y., & Song, M. (2022). Process Layout Improvement through graph division, node rank, and order change (그래프 분할 및 노드 랭크, 오더 변경을 통한 프로세스 레이아웃 개선 연구). In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1.
@inproceedings{conf2022_6,
author = {Lee, D. and Jang, Y. and Song, M.},
title = {Process Layout Improvement through graph division, node rank, and order change (그래프 분할 및 노드 랭크, 오더 변경을 통한 프로세스 레이아웃 개선 연구)},
journal = {In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1},
year = {2022}
}
Jang, Y., Lim, J., & Song, M. (2022). Concept drift in process mining using SGT embedding (SGT embedding을 활용한 프로세스 마이닝에서의 concept drift도출 연구). In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1.
@inproceedings{conf2022_7,
author = {Jang, Y. and Lim, J. and Song, M.},
title = {Concept drift in process mining using SGT embedding (SGT embedding을 활용한 프로세스 마이닝에서의 concept drift도출 연구)},
journal = {In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1},
year = {2022}
}
Hwang, W., Park, K., Lim, J., Lee, D., & Song, M. (2022). Data Requirement Analysis for a Digital Twin Generation in Smart Traffic Control Systems (신호체계의 디지털 트윈 모델 생성을 위한 데이터 요구사항 조사). In Korean Institute of Industrial Engineers (대한산업공학회), Incheon, Korea, November 4-5.
@inproceedings{conf2022_3,
author = {Hwang, W. and Park, K. and Lim, J. and Lee, D. and Song, M.},
title = {Data Requirement Analysis for a Digital Twin Generation in Smart Traffic Control Systems (신호체계의 디지털 트윈 모델 생성을 위한 데이터 요구사항 조사)},
journal = {In Korean Institute of Industrial Engineers (대한산업공학회), Incheon, Korea, November 4-5},
year = {2022}
}
Park, J., Hwang, W., & Song, M. (2022). Enhancement of fashion outfit data using Conditional BERT (Conditional BERT를 활용한 패션 아웃핏 데이터 증진). In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1.
@inproceedings{conf2022_8,
author = {Park, J. and Hwang, W. and Song, M.},
title = {Enhancement of fashion outfit data using Conditional BERT (Conditional BERT를 활용한 패션 아웃핏 데이터 증진)},
journal = {In Korean Industrial Engineering and Management Science (대한산업공학회), Jeju, Korea, June 1},
year = {2022}
}
Kim, H., Ryu, H., Eom, M., Shin, J., Lee, S., Lee, D., Kim, B., Song, M., Yu, Y., Kim, S., & Lee, H. (2021). Hydrogen Refueling Stations Deployment Plan for Hydrogen Mobility. In KORMS/KIIE Conference, International Convention Center (대한산업공학회), Jeju, Korea, June 2-5.
@inproceedings{conf2021_5,
author = {Kim, H. and Ryu, H. and Eom, M. and Shin, J. and Lee, S. and Lee, D. and Kim, B. and Song, M. and Yu, Y. and Kim, S. and Lee, H.},
title = {Hydrogen Refueling Stations Deployment Plan for Hydrogen Mobility},
journal = {In KORMS/KIIE conference, International Convention Center (대한산업공학회), Jeju, Korea, June 2-5},
year = {2021}
}
Lee, D., & Song, M. (2021). Blockchain-based Federated Development of Clinical Pathway. In Korea Bigdata Society Conference, Seoul, Korea, October 29.
@inproceedings{conf2021_3,
author = {Lee, D. and Song, M.},
title = {Blockchain-based Federated Development of Clinical Pathway},
journal = {In Korea Bigdata Society Conference, Seoul, Korea, October 29},
year = {2021}
}
Jang, Y., Lee, D., Song, M., & Kim, S. (2021). Process Layout Improvement using Cross Minimization. In KIIE Conference (대한산업공학회), Dongkuk University, Seoul, Korea, November 12.
@inproceedings{conf2021_2,
author = {Jang, Y. and Lee, D. and Song, M. and Kim, S.},
title = {Process Layout Improvement using Cross Minimization},
journal = {In KIIE conference (대한산업공학회), Dongkuk University, Seoul, Korea, November 12},
year = {2021}
}
Jeong, M., & Song, M. (2021). Similar Situations Search based on Coordinate Data of Players. In KIIE Conference (대한산업공학회), Dongkuk University, Seoul, Korea, November 12.
@inproceedings{conf2021_1,
author = {Jeong, M. and Song, M.},
title = {Similar Situations Search based on Coordinate Data of Players},
journal = {In KIIE conference (대한산업공학회), Dongkuk University, Seoul, Korea, November 12},
year = {2021}
}
Kim, H., Kim, H., Lee, D., Shin, J., Kim, B., Song, M., Lee, S., Ryu, H., Kim, S., Yu, Y., & Lee, H. (2021). Deployment Optimization of Off-site/On-site Hydrogen Refueling Stations. In 2021 Fall Conference of the Korean Hydrogen & New Energy Society, Jeju, Korea.
@inproceedings{conf2021_4,
author = {Kim, H. and Kim, H. and Lee, D. and Shin, J. and Kim, B. and Song, M. and Lee, S. and Ryu, H. and Kim, S. and Yu, Y. and Lee, H.},
title = {Deployment Optimization of Off-site/On-site Hydrogen Refueling Stations},
journal = {In 2021 Fall Conference of the Korean Hydrogen \& New Energy Society, Jeju, Korea},
year = {2021}
}
Lim, J., Kim, K., Cho, M., Baek, H., Kim, S., Hwang, H., Yoo, S., & Song, M. (2021). Deriving a Sophisticated Clinical Pathway Based on Patient Conditions from Electronic Health Record Data. Lecture Notes in Business Information Processing, 406 LNBIP, 356–367.
@inproceedings{Lim2021356,
author = {Lim, J. and Kim, K. and Cho, M. and Baek, H. and Kim, S. and Hwang, H. and Yoo, S. and Song, M.},
title = {Deriving a Sophisticated Clinical Pathway Based on Patient Conditions from Electronic Health Record Data},
journal = {Lecture Notes in Business Information Processing},
year = {2021},
volume = {406 LNBIP},
pages = {356-367},
doi = {10.1007/978-3-030-72693-5_27},
document_type = {Conference Paper},
source = {Scopus},
bpa = {Best Paper Award, Scopus}
}
Lim, J., Kim, K., Cho, M., Baek, H., Kim, S., Hwang, H., Yoo, S., & Song, M. (2021). Deriving a Sophisticated Clinical Pathway Based on Patient Conditions from Electronic Health Record Data. Lecture Notes in Business Information Processing, 406 LNBIP, 356–367.
@inproceedings{Lim2021357,
author = {Lim, J. and Kim, K. and Cho, M. and Baek, H. and Kim, S. and Hwang, H. and Yoo, S. and Song, M.},
title = {Deriving a Sophisticated Clinical Pathway Based on Patient Conditions from Electronic Health Record Data},
journal = {Lecture Notes in Business Information Processing},
year = {2021},
volume = {406 LNBIP},
pages = {356-367},
doi = {10.1007/978-3-030-72693-5_27},
document_type = {Conference Paper},
source = {Scopus},
bpa = {Best Paper Award, Scopus}
}
Jeong, M., & Song, M. (2021). Football Strategy Analysis using Video Data. Workshop of the Korea BigData Society, Silla University, Busan, Korea, May 1.
@inproceedings{conf3,
author = {Jeong, M. and Song, M.},
title = {Football Strategy Analysis using Video Data.},
journal = {Workshop of the Korea BigData Society, Silla University, Busan, Korea, May 1},
year = {2021}
}
Kim, H., Kim, H., Lee, D., Shin, J., Kim, B., Song, M., Lee, S., Ryu, H., Kim, S., Ryu, Y., & Lee, H. (2021). Off-site/on-site refueling station layout optimization for optimal hydrogen supply path design. In Korea Hydrogen and New Energy Society Fall Conference, Jeju, Korea, October 20-22.
@inproceedings{conf101,
author = {Kim, H. and Kim, H. and Lee, D. and Shin, J. and Kim, B. and Song, M. and Lee, S. and Ryu, H. and Kim, S. and Ryu, Y. and Lee, H.},
title = {Off-site/on-site refueling station layout optimization for optimal hydrogen supply path design.},
journal = {In Korea Hydrogen and New Energy Society Fall Conference, Jeju, Korea, October 20-22},
year = {2021}
}
Kim, H., Ryu, H., Eom, M., Shin, J., Lee, S., Lee, D., Kim, B., Song, M., Ryu, Y., Kim, S., & Lee, H. (2021). Optimizing Hydrogen Station Location for Mobility. In KORMS/KIIE Conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5.
@inproceedings{conf100,
author = {Kim, H. and Ryu, H. and Eom, M. and Shin, J. and Lee, S. and Lee, D. and Kim, B. and Song, M. and Ryu, Y. and Kim, S. and Lee, H.},
title = {Optimizing Hydrogen Station Location for Mobility.},
journal = {In KORMS/KIIE conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5},
year = {2021}
}
Lee, D., & Song, M. (2021). Blockchain-based Health Information Exchange using Ring Signature & Stealth Address. In KORMS/KIIE Conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5.
@inproceedings{conf2,
author = {Lee, D. and Song, M.},
title = {Blockchain-based Health Information Exchange using Ring Signature \& Stealth Address.},
journal = {In KORMS/KIIE conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5},
year = {2021}
}
Hwang, W., Park, J., & Song, M. (2021). Fashion Outfit Recommendation using Multi Bi-LSTM models. In KORMS/KIIE Conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5.
@inproceedings{conf1,
author = {Hwang, W. and Park, J. and Song, M.},
title = {Fashion Outfit Recommendation using Multi Bi-LSTM models.},
journal = {In KORMS/KIIE conference (대한산업공학회), International Convention Center, Jeju, Korea, June 2-5},
year = {2021}
}
Ryu, D., Ko, Y., Kim, Y., Song, M., & Kim, K. (2020). Household Assignment Considering Appliance Noise Discomfort between Neighbors and Housing Preference: A Case Study of a Campus Apartment Building. INFORMS Annual Meeting, Virtual, November 7-13.
@inproceedings{RyuInform2020,
author = {Ryu, D. and Ko, Y. and Kim, Y. and Song, M. and Kim, K.},
title = {Household Assignment Considering Appliance Noise Discomfort between Neighbors and Housing Preference: A Case Study of a Campus Apartment Building},
journal = {INFORMS Annual Meeting, Virtual, November 7-13},
year = {2020},
document_type = {Conference Paper}
}
When households move into empty units in a collective residential building, appliance noise discomfort (ND) between neighbors and housing preference (HP) are important considerations. This study proposes a model to assign households with the goal of minimizing ND and unsatisfied HP, and demonstrates the application of the model through a case study at a campus apartment building. ND levels are calculated by an approach that utilizes electricity usage data and identifies time difference in appliance usage between neighbors. Items of HP are extracted from interviews with residents living in the building. This study discusses assignment results under various scenarios through sensitivity analysis. The proposed model can help to assign households considering ND and HP in a collective building in practice.
Ryu, D., Ko, Y., Kim, Y., Song, M., & Kim, K. (2020). Collection and Analysis of Electricity Usage Data in University Campus: The Case of POSTECH. Proceedings of The 18th ANQ Congress 2020, Virtual October 22-23.
@inproceedings{RyuANQ2020,
author = {Ryu, D. and Ko, Y. and Kim, Y. and Song, M. and Kim, K.},
title = {Collection and Analysis of Electricity Usage Data in University Campus: The Case of POSTECH},
journal = {Proceedings of The 18th ANQ Congress 2020, Virtual October 22-23},
year = {2020},
document_type = {Conference Paper}
}
Advanced metering infrastructure (AMI) refers to an integrated system of smart meters, communication networks, and data management systems. Pohang University of Science Technology (POSTECH) has developed its own AMI for electricity usage data from campus buildings. POSTECH has also developed an IT platform, called Open Innovation Bigdata Center (OIBC), to store and share various data made in the campus. In this work, we describe the AMI and the OIBC platform that include various sensors and systems for measuring, storing, calling, and monitoring data. The data are collected from seven buildings that have different characteristics. We also present some applications of the collected data. The applications show that the amounts of electricity usage of the seven campus buildings are different depending on various factors, including the building’s size, uses, and occupant type and their behaviors. The information extracted from the applications can be used to improve the satisfaction of students and faculty as well as the efficiency in electricity management.
Park, S., & Song, M. (2020). Manufacturing Process Simulation using Event-log. In Society for Computer Design and Engineering, Online Conference, Korea, August 17-18.
@inproceedings{conf4,
author = {Park, S. and Song, M.},
title = {Manufacturing Process Simulation using Event-log.},
journal = {In Society for Computer Design and Engineering, Online Conference, Korea, August 17-18},
year = {2020},
bpa = {Best Paper Award}
}
Ryu, D., Ko, Y., Kim, Y., Song, M., & Kim, K. (2020). Collection and Analysis of Electricity Consumption Data: The Case of POSTECH Campus. Proceedings of INFORMS Conference on Service Science, Virtual, December 19-21.
@inproceedings{RyuService2020,
author = {Ryu, D. and Ko, Y. and Kim, Y. and Song, M. and Kim, K.},
title = {Collection and Analysis of Electricity Consumption Data: The Case of POSTECH Campus},
journal = {Proceedings of INFORMS Conference on Service Science, Virtual, December 19-21},
year = {2020},
document_type = {Conference Paper}
}
Advanced metering infrastructure (AMI) is an integrated system of smart meters, communication networks, and data management systems. The AMI allows the automatic and remote measurement and monitoring of energy consumption. It also provides important information for the management of peak demand and energy consumption and costs. Pohang University of Science Technology (POSTECH) has developed its own AMI and an IT platform called Open Innovation Big Data Center (OIBC) to store and share various data collected in the campus. In this work, we describe the AMI and the OIBC platform equipped with various sensors and systems for measuring, storing, calling, and monitoring data. Data are collected from seven buildings with different characteristics. We installed 266 sensors at the buildings, including 188 EnerTalk and Biz, 18 plugin, and 60 high-sampling sensors. The sensors collect electricity consumption data in real time, and users can visualize and download the data through the OIBC platform. In this work, we present analysis results of the collected data. The results show that the amounts of electricity consumed by campus buildings are different depending on various factors, including building size, occupant type and their behaviors, and building use. We also compare the amounts of electricity consumed before and after the COVID-19 outbreak. The information extracted can be used to improve the satisfaction of students and faculty as well as the efficiency of electricity management.
Park, G., Cho, M., Song, M., & Lee, J. (2019). Data Analysis using Logistics Data Warehouse in Semiconductor Manufacturing. In KORMS/KIIE Conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13.
@inproceedings{conf8,
author = {Park, G. and Cho, M. and Song, M. and Lee, J.},
title = {Data Analysis using Logistics Data Warehouse in Semiconductor Manufacturing.},
journal = {In KORMS/KIIE conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13},
year = {2019}
}
Na, Y., Kim, B., Song, M., & Kim, J. (2019). Resource-constrained Project Scheduling Problem in a Real-world Heavy Industry Company. 2019 INFORMS Annual Meeting (Seattle, Washington, USA), October 20-23.
@inproceedings{yna2019,
author = {Na, Y. and Kim, B. and Song, M. and Kim, J.},
title = {Resource-constrained Project Scheduling Problem in a Real-world Heavy Industry Company},
journal = {2019 INFORMS Annual meeting (Seattle, Washington, USA), October 20-23},
year = {2019}
}
Park, G., & Song, M. (2019). Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm. Proceedings - 2019 International Conference on Process Mining, ICPM 2019, 121–128.
@inproceedings{Park2019121,
author = {Park, G. and Song, M.},
title = {Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm},
journal = {Proceedings - 2019 International Conference on Process Mining, ICPM 2019},
year = {2019},
pages = {121-128},
doi = {10.1109/ICPM.2019.00027},
art_number = {8786063},
document_type = {Conference Paper},
source = {Scopus}
}
Cho, M., Song, M., Yeom, S.-R., Wang, I.-J., & Choi, B.-K. (2019). Developing Process Performance Indicators for Emergency Room Processes. Lecture Notes in Business Information Processing, 362 LNBIP, 520–531.
@inproceedings{Cho2019520,
author = {Cho, M. and Song, M. and Yeom, S.-R. and Wang, I.-J. and Choi, B.-K.},
title = {Developing Process Performance Indicators for Emergency Room Processes},
journal = {Lecture Notes in Business Information Processing},
year = {2019},
volume = {362 LNBIP},
pages = {520-531},
doi = {10.1007/978-3-030-37453-2_42},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Lee, D., & Song, M. (2019). An Application of Blockchain Technology in Healthcare Industry. In KORMS/KIIE Conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13.
@inproceedings{conf11,
author = {Lee, D. and Song, M.},
title = {An Application of Blockchain Technology in Healthcare Industry.},
journal = {In KORMS/KIIE conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13},
year = {2019}
}
Lim, J., Hwang, W., Cho, M., Song, M., Yoo, S., & Kim, G. (2019). CP Development Based on Patient Care Data Using Matching Rate. In KORMS/KIIE Conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13.
@inproceedings{conf10,
author = {Lim, J. and Hwang, W. and Cho, M. and Song, M. and Yoo, S. and Kim, G.},
title = {CP Development Based on Patient Care Data Using Matching Rate.},
journal = {In KORMS/KIIE conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13},
year = {2019}
}
Seo, J., Kim, S., Song, M., Lee, H., & Park, Y. (2019). FSM based Feature Engineering in Purchase Prediction. In KORMS/KIIE Conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13.
@inproceedings{conf9,
author = {Seo, J. and Kim, S. and Song, M. and Lee, H. and Park, Y.},
title = {FSM based Feature Engineering in Purchase Prediction.},
journal = {In KORMS/KIIE conference, Gwangju Kimdaejung Convention Center, Gwangju, Korea, April 10-13},
year = {2019}
}
Cho, M., Lim, J., Song, M., Yoo, S., Kim, K., & Baek, H. (2019). Deriving CP using EHR(Electronic Health Records) Data. In The Korean Society of Medical Informatics, Korea University College of Medicine, Seoul, Korea, July 11-12.
@inproceedings{conf7,
author = {Cho, M. and Lim, J. and Song, M. and Yoo, S. and Kim, K. and Baek, H.},
title = {Deriving CP using EHR(Electronic Health Records) Data.},
journal = {In The Korean Society of Medical Informatics, Korea University College of Medicine, Seoul, Korea, July 11-12},
year = {2019},
bpa = {Best Paper Award}
}
Kim, R., Ryu, D., Song, M., Choi, D., Ko, Y., Kim, Y., & Kim, K. (2019). 캠퍼스 전력 사용 데이터 기반의 기숙사 호실 배정 서비스 컨셉 개발. Proceedings of 2019 Fall Conference of Korean Institute of Industrial Engineers, Seoul, Korea, November 8.
@inproceedings{kim_kiie_2019,
author = {Kim, R. and Ryu, D. and Song, M. and Choi, D. and Ko, Y. and Kim, Y. and Kim, K.},
title = {캠퍼스 전력 사용 데이터 기반의 기숙사 호실 배정 서비스 컨셉 개발.},
booktitle = {Proceedings of 2019 Fall Conference of Korean Institute of Industrial Engineers, Seoul, Korea, November 8},
year = {2019}
}
대학 캠퍼스 건물은 용도에 따라 사무실, 강의실, 기숙사 등으로 구성되며, 학생, 교직원, 연구원 등의 구성원들이 많은 전기를 사용한다. 포스텍은 Open Innovation Bigdata Center (OIBC)를 설립하고 2017년부터 교내의 7개 건물을 대상으로 실시간 전력 사용 데이터를 수집하고 있다. 본 연구에서는 캠퍼스 빌딩의 전력 사용과 관련된 서비스 컨셉을 개발한다. 이를 위해, 문헌조사와 교내 구성원 인터뷰를 통해 서비스 아이디어를 도출하고, 수집된 전력 사용 데이터를 기반으로 아이디어를 고도화하여 서비스 컨셉을 개발하였다. 이후 교내 구성원들의 평가를 통해 그 효용성을 검증한다. 본 연구의 결과물은 캠퍼스 내 전력 사용량 절감 효과와 더불어 구성원들의 캠퍼스 내 생활 만족도를 향상시킬 것으로 기대된다.
Park, S., & Song, M. (2019). BHS(Baggage Handling System) Analysis using Process Mining. In KORMS Conference, Korea Aerospace University, Seoul, Korea, October 25.
@inproceedings{conf6,
author = {Park, S. and Song, M.},
title = {BHS(Baggage Handling System) Analysis using Process Mining.},
journal = {In KORMS conference, Korea Aerospace University, Seoul, Korea, October 25},
year = {2019}
}
Cho, H., Kim, D., You, D., Song, M., Choi, N., Yoon, C., & Park, J. (2019). Prediction of gas usages based on Machine Learning. In KIIE Conference (대한산업공학회), Seoul National University, Seoul, Korea, November 8.
@inproceedings{conf5,
author = {Cho, H. and Kim, D. and You, D. and Song, M. and Choi, N. and Yoon, C. and Park, J.},
title = {Prediction of gas usages based on Machine Learning.},
journal = {In KIIE Conference (대한산업공학회), Seoul National University, Seoul, Korea, November 8},
year = {2019}
}
Wynn, M. T., Comuzzi, M., Song, M., & Wen, L. (2018). Introduction to the first international workshop on quality data for process analytics (QD-PA 2017). Lecture Notes in Business Information Processing, 308, 569–572.
@inproceedings{Wynn2018569,
author = {Wynn, M.T. and Comuzzi, M. and Song, M. and Wen, L.},
title = {Introduction to the first international workshop on quality data for process analytics (QD-PA 2017)},
journal = {Lecture Notes in Business Information Processing},
year = {2018},
volume = {308},
pages = {569-572},
document_type = {Editorial},
source = {Scopus}
}
Lee, M., Kwak, J., Han, S., Jeong, M. S. D., Cho, M., Kim, D., & Lee, D. (2018). Customer Requirement Analysis of Intelligent Services in IoT Environment. In Spring Conference of the Ergonomics Society of Korea, Jeju Phoenix Resort, Jeju, Korea, May 16-19.
@inproceedings{conf12,
author = {Lee, M. and Kwak, J. and Han, S. and Jeong, M. Song D. and Cho, M. and Kim, D. and Lee, D.},
title = {Customer Requirement Analysis of Intelligent Services in IoT Environment.},
journal = {In Spring Conference of the Ergonomics Society of Korea, Jeju Phoenix Resort, Jeju, Korea, May 16-19},
year = {2018}
}
Park, G., Cho, M., Song, M., & Lee, J. (2018). Deriving an Optimal Routing for Yield Enhancement in Semiconductor Manufacturing. In KORMS/KIIE Conference, Gyeongju Hotel Hyundai, Gyeongju, Korea, April 04-07.
@inproceedings{conf13,
author = {Park, G. and Cho, M. and Song, M. and Lee, J.},
title = {Deriving an Optimal Routing for Yield Enhancement in Semiconductor Manufacturing.},
journal = {In KORMS/KIIE conference, Gyeongju Hotel Hyundai, Gyeongju, Korea, April 04-07},
year = {2018}
}
Lim, J., Song, M., Cho, M., Yoo, S., Kim, K., Baek, H., & Kim, S. (2018). Deriving CP Variations by Patient Characteristics using Medical Outcome Analysis. In KORMS/KIIE Conference, Gyeongju Hotel Hyundai, Gyeongju, Korea, April 04-07.
@inproceedings{conf14,
author = {Lim, J. and Song, M. and Cho, M. and Yoo, S. and Kim, K. and Baek, H. and Kim, S.},
title = {Deriving CP Variations by Patient Characteristics using Medical Outcome Analysis.},
journal = {In KORMS/KIIE conference, Gyeongju Hotel Hyundai, Gyeongju, Korea, April 04-07},
year = {2018}
}
Lee, D., Gorvacheva, E., Song, M., Jung, S. Y., & Yoo, S. (2018). Healthcare processes supported by blockchain: a literature review. International Conference on Asia Pacific Industrial Engineering & Management Systems(APIEMS) 2018.
@inproceedings{Lee2018,
author = {Lee, D. and Gorvacheva, E. and Song, M. and Jung, S.Y. and Yoo, S.},
title = {Healthcare processes supported by blockchain: a literature review},
journal = {International Conference on Asia Pacific Industrial Engineering \& Management Systems(APIEMS) 2018},
year = {2018},
document_type = {Conference Paper}
}
Na, Y., Kim, B., Song, M., & Kim, J. (2018). Critical Path Optimization for the Shipbuilding Industry. 2018 APIEMS Conference(Hong Kong), December 5-8.
@inproceedings{yna2018,
author = {Na, Y. and Kim, B. and Song, M. and Kim, J.},
title = {Critical Path Optimization for the Shipbuilding Industry},
journal = {2018 APIEMS Conference(Hong Kong), December 5-8},
year = {2018}
}
Cho, M., Song, M., Müller, C., Fernandez, P., del-Río-Ortega, A., Resinas, M., & Ruiz-Cortés, A. (2017). A new framework for defining realistic SLAs: An evidence-based approach. Lecture Notes in Business Information Processing, 297, 19–35.
@inproceedings{Cho201719,
author = {Cho, M. and Song, M. and Müller, C. and Fernandez, P. and del-Río-Ortega, A. and Resinas, M. and Ruiz-Cortés, A.},
title = {A new framework for defining realistic SLAs: An evidence-based approach},
journal = {Lecture Notes in Business Information Processing},
year = {2017},
volume = {297},
pages = {19-35},
doi = {10.1007/978-3-319-65015-9_2},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Müller, C., Fernandez, P., Song, M., Resinas, M., & Ruiz-Cortés, A. (2017). Devising an SLA-Aware Methodology to Improve Process Performance. Jornadas De Ciencia e Ingeniería De Servicios, Spain, July 19-21.
@inproceedings{muller2017,
author = {Müller, C. and Fernandez, P. and Song, M. and Resinas, M. and Ruiz-Cortés, A.},
title = {Devising an SLA-Aware Methodology to Improve Process Performance},
journal = {Jornadas de Ciencia e Ingeniería de Servicios, Spain, July 19-21},
year = {2017}
}
Jang, H., Kwahk, J., Han, S., Song, M., Choi, D., Park, K., Kim, D., Won, Y., & Jeong, I. (2017). Suggesting Design Method for Performance Evaluation System Based on IoT Data: Considering UX. Proceedings of the Tenth International Conference on Advances in Computer-Human Interactions 2017.
@inproceedings{Jang2017,
author = {Jang, H. and Kwahk, J. and Han, S. and Song, M. and Choi, D. and Park, K. and Kim, D. and Won, Y. and Jeong, I.},
title = {Suggesting Design Method for Performance Evaluation System Based on IoT Data: Considering UX},
journal = {Proceedings of the Tenth International Conference on Advances in Computer-Human Interactions 2017},
year = {2017}
}
Song, M., Wynn, M. T., & Liu, J. (2017). Preface. Lecture Notes in Business Information Processing, 159, VI.
@inproceedings{Song2017VI,
author = {Song, M. and Wynn, M.T. and Liu, J.},
title = {Preface},
journal = {Lecture Notes in Business Information Processing},
year = {2017},
volume = {159},
pages = {VI},
document_type = {Editorial},
source = {Scopus}
}
Park, K., Han, S., Kwak, J., Song, M., Choi, D., Jang, H., Kim, D., Won, Y., & Jeong, I. (2017). Performance Indicators in LKAS. Proceedings of Spring Conference of the Ergonomics Society of Korea, Jeju Kensington Resort, Jeju, Korea, April 27-30, 2017.
@inproceedings{conf15,
author = {Park, K. and Han, S. and Kwak, J. and Song, M. and Choi, D. and Jang, H. and Kim, D. and Won, Y. and Jeong, I.},
title = {Performance Indicators in LKAS.},
booktitle = {Proceedings of Spring Conference of the Ergonomics Society of Korea, Jeju Kensington Resort, Jeju, Korea, April 27-30, 2017},
year = {2017}
}
Ryu, D., Kim, K., Ko, Y., & Song, M. (2017). A Review of Case Studies: Service Development by Utilizing Energy Usage Data. Proceedings of 2017 Spring Conference of Korean Institute of Industrial Engineers, Yeosu, Korea, April 26-29, 2017.
@inproceedings{conf17_01,
author = {Ryu, D. and Kim, K. and Ko, Y. and Song, M.},
title = {A Review of Case Studies: Service Development by Utilizing Energy Usage Data},
booktitle = {Proceedings of 2017 Spring Conference of Korean Institute of Industrial Engineers, Yeosu, Korea, April 26-29, 2017},
year = {2017}
}
최근 에너지 고갈에 대한 우려와 IT 기술의 발전이 맞물려 다양한 에너지 절약 및 관리 시스템이 등장하고 있다. 또한, 마이크로그리드와 같이 소규모 독립형 전력망 구축이 가능해지고 센서를 통한 실시간 에너지 사용 데이터 수집이 가능해지면서 이를 활용한 에너지 절약 및 관리 서비스의 관심과 경쟁이 급증하고 있다. 본 연구에서는 에너지 사용 데이터를 활용한 시스템 구축 및 서비스 개발 사례 연구 리뷰를 통해 트렌드를 정리하고 추후 연구 이슈를 제안한다. 본 연구는 에너지 사용 데이터 활용을 통한 시스템 구축 및 서비스 개발 시 참고 자료로 활용될 수 있을 것이며, 나아가 스마트 시티, 스마트 팩토리 등 다양한 분야에서 에너지 사용 데이터를 통한 잠재적 서비스 발굴의 기회를 제공할 수 있을 것이다.
Tu, T. B. H., & Song, M. (2016). Analysis and prediction cost of manufacturing process based on process mining. ICIMSA 2016 - 2016 3rd International Conference on Industrial Engineering, Management Science and Applications.
@inproceedings{Tu2016,
author = {Tu, T.B.H. and Song, M.},
title = {Analysis and prediction cost of manufacturing process based on process mining},
journal = {ICIMSA 2016 - 2016 3rd International Conference on Industrial Engineering, Management Science and Applications},
year = {2016},
doi = {10.1109/ICIMSA.2016.7503993},
art_number = {7503993},
document_type = {Conference Paper},
source = {Scopus}
}
Lee, Y., & Song, M. (2016). A Reference Model for Big Data Analysis in Shipbuilding. In KIIE Conference, Korea University, Seoul, Korea, November 19.
@inproceedings{conf16,
author = {Lee, Y. and Song, M.},
title = {A Reference Model for Big Data Analysis in Shipbuilding.},
journal = {In KIIE Conference, Korea University, Seoul, Korea, November 19},
year = {2016}
}
Yi, H., Cho, M., Kim, D., & Song, M. (2016). An application of Process mining on Business Process Re-engineering: A case study? . In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16.
@inproceedings{conf19,
author = {Yi, H. and Cho, M. and Kim, D. and Song, M.},
title = {An application of Process mining on Business Process Re-engineering: A case study? },
journal = {In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16},
year = {2016}
}
Hong, T., & Song, M. (2016). Process Mining-driven Efficient Manufacturing Cost Management. In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16.
@inproceedings{conf18,
author = {Hong, T. and Song, M.},
title = {Process Mining-driven Efficient Manufacturing Cost Management.},
journal = {In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16},
year = {2016}
}
Cho, M., Song, M., & Yoo, S. (2016). Personal clinician scheduling using Discrete Event Simulation based on Process Mining. In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16.
@inproceedings{conf17,
author = {Cho, M. and Song, M. and Yoo, S.},
title = {Personal clinician scheduling using Discrete Event Simulation based on Process Mining.},
journal = {In KORMS/KIIE/ESK/KSIE/KSS Conference, International Convention Center, Jeju, Korea, April 13-16},
year = {2016}
}
Cho, M., Song, M., & Yoo, S. (2015). A Method for Developing Clinical Pathway using Event Logs. In KORMS/KIIE/ESK/KSIE/KSS Conference, Ramada Hotel, Jeju, Korea, April 8-11.
@inproceedings{conf20,
author = {Cho, M. and Song, M. and Yoo, S.},
title = {A Method for Developing Clinical Pathway using Event Logs.},
journal = {In KORMS/KIIE/ESK/KSIE/KSS Conference, Ramada Hotel, Jeju, Korea, April 8-11},
year = {2015}
}
Park, M., Song, M., Baek, T. H., Son, S. Y., Ha, S. J., & Cho, S. W. (2015). Workload and delay analysis in manufacturing process using process mining. Lecture Notes in Business Information Processing, 219, 138–151.
@inproceedings{Park2015138,
author = {Park, M. and Song, M. and Baek, T.H. and Son, S.Y. and Ha, S.J. and Cho, S.W.},
title = {Workload and delay analysis in manufacturing process using process mining},
journal = {Lecture Notes in Business Information Processing},
year = {2015},
volume = {219},
pages = {138-151},
doi = {10.1007/978-3-319-19509-4_11},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Yang, H., Hong, S., & Song, M. (2014). Process Improvement based on Best Practices and Social Network Analysis. In KORMS/KIIE Conference, Bexco, Busan, Korea, May 16-17.
@inproceedings{conf22,
author = {Yang, H. and Hong, S. and Song, M.},
title = {Process Improvement based on Best Practices and Social Network Analysis.},
journal = {In KORMS/KIIE conference, Bexco, Busan, Korea, May 16-17},
year = {2014}
}
Yang, H., Yoon, Y., & Song, M. (2014). Production efficiency and process quality analysis for manufacturing process based on event logs. In The Korean Society of Manufacturing Technology Engineers, Jeju KAL Hotel, Jeju, Korea, September 29-30.
@inproceedings{conf21,
author = {Yang, H. and Yoon, Y. and Song, M.},
title = {Production efficiency and process quality analysis for manufacturing process based on event logs.},
journal = {In The Korean Society of Manufacturing Technology Engineers, Jeju KAL Hotel, Jeju, Korea, September 29-30},
year = {2014}
}
Yang, H., & Song, M. (2014). Analyzing service processes using process mining: A case study. The 15th Asia Pacific Industrial Engineering and Management Systems, Ramada Plaza Jeju Hotel, Jeju, Korea, October 12-15.
@inproceedings{yang2014,
author = {Yang, H. and Song, M.},
title = {Analyzing service processes using process mining: A case study},
journal = {The 15th Asia Pacific Industrial Engineering and Management Systems, Ramada Plaza Jeju Hotel, Jeju, Korea, October 12-15},
year = {2014},
document_type = {Conference Paper}
}
Park, M., Song, M., & Kwon, D. (2014). Prediction for material usage using decision tree. The 15th Asia Pacific Industrial Engineering and Management Systems, Ramada Plaza Jeju Hotel, Jeju, Korea, October 12-15.
@inproceedings{park20,
author = {Park, M. and Song, M. and Kwon, D.},
title = {Prediction for material usage using decision tree},
journal = {The 15th Asia Pacific Industrial Engineering and Management Systems, Ramada Plaza Jeju Hotel, Jeju, Korea, October 12-15},
year = {2014},
document_type = {Conference Paper}
}
Son, S., Yahya, B., & Song, M. (2014). A Manufacturing Resource Analysis in Configurable Manufacturing Execution Systems. International Symposium on Green Manufacturing and Applications, Paradise Hotel, Busan, Korea, June 24-28.
@inproceedings{son2014,
author = {Son, S. and Yahya, B. and Song, M.},
title = {A Manufacturing Resource Analysis in Configurable Manufacturing Execution Systems},
journal = {International Symposium on Green Manufacturing and Applications, Paradise Hotel, Busan, Korea, June 24-28},
year = {2014},
document_type = {Conference Paper}
}
Cho, M., Song, M., & Yoo, S. (2014). A systematic methodology for outpatient process analysis based on process mining. Lecture Notes in Business Information Processing, 181 LNBIP, 31–42.
@inproceedings{Cho201431,
author = {Cho, M. and Song, M. and Yoo, S.},
title = {A systematic methodology for outpatient process analysis based on process mining},
journal = {Lecture Notes in Business Information Processing},
year = {2014},
volume = {181 LNBIP},
pages = {31-42},
doi = {10.1007/978-3-319-08222-6_3},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Yang, H., Park, M., Cho, M., Song, M., & Kim, S. (2014). A system architecture for manufacturing process analysis based on big data and process mining techniques. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, 1024–1029.
@inproceedings{Yang20141024,
author = {Yang, H. and Park, M. and Cho, M. and Song, M. and Kim, S.},
title = {A system architecture for manufacturing process analysis based on big data and process mining techniques},
journal = {Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014},
year = {2014},
pages = {1024-1029},
doi = {10.1109/BigData.2014.7004336},
art_number = {7004336},
document_type = {Conference Paper},
source = {Scopus}
}
Lohmann, N., Song, M., & Wohed, P. (2014). Business Process Management Workshops: BPM 2013 international workshops Beijing, China, August 26, 2013 revised papers. Lecture Notes in Business Information Processing, 171 171 LNBIP.
@inproceedings{Lohmann2014,
author = {Lohmann, N. and Song, M. and Wohed, P.},
title = {Business Process Management Workshops: BPM 2013 international workshops Beijing, China, August 26, 2013 revised papers},
journal = {Lecture Notes in Business Information Processing},
year = {2014},
volume = {171 171 LNBIP},
document_type = {Conference Paper},
source = {Scopus}
}
Shim, Y., Cho, M., Song, M., & Yoo, S. (2014). Delta analysis for organizational environment using process mining: A case study. In KORMS/KIIE Conference, Bexco, Busan, Korea, May 16-17.
@inproceedings{conf23,
author = {Shim, Y. and Cho, M. and Song, M. and Yoo, S.},
title = {Delta analysis for organizational environment using process mining: A case study.},
journal = {In KORMS/KIIE conference, Bexco, Busan, Korea, May 16-17},
year = {2014}
}
Son, S., Song, M., Cho, Y., Cho, H., & Yahya, B. (2013). Developing a simulation model for manufacturing process using process mining. In The Korean Society of Manufacturing Technology Engineers, Bexco, Busan, Korea, April 25-26.
@inproceedings{conf28,
author = {Son, S. and Song, M. and Cho, Y. and Cho, H. and Yahya, B.},
title = {Developing a simulation model for manufacturing process using process mining.},
journal = {In The Korean Society of Manufacturing Technology Engineers, Bexco, Busan, Korea, April 25-26},
year = {2013}
}
Cho, M., Song, M., & Yoo, S. (2013). Payment Process Analysis Based on Process Mining. In the Korea Intelligent Information System Society Conference, Sogang University, Seoul, Korea, June 1.
@inproceedings{conf26,
author = {Cho, M. and Song, M. and Yoo, S.},
title = {Payment Process Analysis Based on Process Mining.},
journal = {In the Korea Intelligent Information System Society Conference, Sogang University, Seoul, Korea, June 1},
year = {2013}
}
Kim, E., Kim, S., Cho, M., Song, M., & Yoo, S. (2013). Simulation of Optimal Number of Outpatient Payment KIOSK Estimation Using Process Mining. In The Korean Society of Medical Informatics, Asan Hospital, Seoul, Korea, June 13-14.
@inproceedings{conf25,
author = {Kim, E. and Kim, S. and Cho, M. and Song, M. and Yoo, S.},
title = {Simulation of Optimal Number of Outpatient Payment KIOSK Estimation Using Process Mining.},
journal = {In The Korean Society of Medical Informatics, Asan Hospital, Seoul, Korea, June 13-14},
year = {2013},
bpa = {Best Paper Award}
}
Hong, S., Yang, H., Kim, S., & Song, M. (2013). Business performance analysis using process mining. In KIIE Conference, Sungkyunkwan University, Seoul, Korea, November 15.
@inproceedings{conf24,
author = {Hong, S. and Yang, H. and Kim, S. and Song, M.},
title = {Business performance analysis using process mining.},
journal = {In KIIE Conference, Sungkyunkwan University, Seoul, Korea, November 15},
year = {2013}
}
Song, M., Yahya, B. N., Cho, H. J., Cho, Y. J., & and K-Y. Ryu. (2013). Analysis of Manufacturing Processes with Process Mining in Configurable Manufacturing Execution systems. Proceedings on the 2013 International Symposium on Green Manufacturing and Applications, Hawaii, USA, June 25-29.
@inproceedings{Song2013,
author = {Song, M. and Yahya, B.N. and Cho, H.J. and Cho, Y.J. and and K-Y. Ryu},
title = {Analysis of Manufacturing Processes with Process Mining in Configurable Manufacturing Execution systems},
journal = {Proceedings on the 2013 International Symposium on Green Manufacturing and Applications, Hawaii, USA, June 25-29},
year = {2013},
document_type = {Conference Paper}
}
Jo, H., Cho, Y., Noh, S., Ryu, K., & Song, M. (2013). A Study of a Production Service Framework based on the Manufacturing Execution System. 17th International Conference on Industrial Engineering Theory, Applications and Practice, Pusan National University, Busan, Korea, October 6-9.
@inproceedings{Song2013_2,
author = {Jo, H. and Cho, Y. and Noh, S. and Ryu, K. and Song, M.},
title = {A Study of a Production Service Framework based on the Manufacturing Execution System},
journal = {17th International Conference on Industrial Engineering Theory, Applications and Practice, Pusan National University, Busan, Korea, October 6-9},
year = {2013},
document_type = {Conference Paper}
}
Kumar, A., Dijkman, R., & Song, M. (2013). Optimal resource assignment in workflows for maximizing cooperation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8094 LNCS, 235–250.
@inproceedings{Kumar2013235,
author = {Kumar, A. and Dijkman, R. and Song, M.},
title = {Optimal resource assignment in workflows for maximizing cooperation},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2013},
volume = {8094 LNCS},
pages = {235-250},
doi = {10.1007/978-3-642-40176-3_20},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Lee, Y., Yahya, B. N., Cho, H., Cho, Y., Ryu, K., & Song, M. (2013). Development of manufacturing process analysis tool for c-MES. In the Korean Society for Precision Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea, October 24-26.
@inproceedings{conf34,
author = {Lee, Y. and Yahya, B. N. and Cho, H. and Cho, Y. and Ryu, K. and Song, M.},
title = {Development of manufacturing process analysis tool for c-MES.},
journal = {In the Korean Society for Precision Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea, October 24-26},
year = {2013}
}
Lee, H., Ryu, K., Song, M., Cho, Y., & Cho, H. (2013). Process Model Based Function Recommendation to Support Design of Configurable Manufacturing Support Systems. In the Korean Society of CAD/CAM Engineers Conference, Phoenix Park, Kangwon, Korea, January 30 - February 1.
@inproceedings{conf29,
author = {Lee, H. and Ryu, K. and Song, M. and Cho, Y. and Cho, H.},
title = {Process Model Based Function Recommendation to Support Design of Configurable Manufacturing Support Systems.},
journal = {In the Korean Society of CAD/CAM Engineers Conference, Phoenix Park, Kangwon, Korea, January 30 - February 1},
year = {2013}
}
Cho, M., Song, M., & Yoo, S. (2013). Healthcare Process Simulation Based on Process Mining. In KORMS/KIIE Conference, The Ocean Resort, Yeosu, Korea, May 24-25.
@inproceedings{conf27,
author = {Cho, M. and Song, M. and Yoo, S.},
title = {Healthcare Process Simulation Based on Process Mining.},
journal = {In KORMS/KIIE conference, The Ocean Resort, Yeosu, Korea, May 24-25},
year = {2013}
}
Yang, H., & Song, M. (2012). Trace clustering with Dimensionality Reduction Techniques. In KORMS/KIIE Conference, Hyndai Hotel, Gyeongju, Korea, May 10-11.
@inproceedings{conf36,
author = {Yang, H. and Song, M.},
title = {Trace clustering with Dimensionality Reduction Techniques.},
journal = {In KORMS/KIIE conference, Hyndai hotel, Gyeongju, Korea, May 10-11},
year = {2012}
}
Van Der Aalst, W., Adriansyah, A., De Medeiros, A. K. A., Arcieri, F., Baier, T., Blickle, T., Bose, J. C., Van Den Brand, P., Brandtjen, R., Buijs, J., Burattin, A., Carmona, J., Castellanos, M., Claes, J., Cook, J., Costantini, N., Curbera, F., Damiani, E., De Leoni, M., … Wynn, M. (2012). Process mining manifesto. Lecture Notes in Business Information Processing, 99 LNBIP(PART 1), 169–194.
@inproceedings{VanDerAalst2012169,
author = {Van Der Aalst, W. and Adriansyah, A. and De Medeiros, A.K.A. and Arcieri, F. and Baier, T. and Blickle, T. and Bose, J.C. and Van Den Brand, P. and Brandtjen, R. and Buijs, J. and Burattin, A. and Carmona, J. and Castellanos, M. and Claes, J. and Cook, J. and Costantini, N. and Curbera, F. and Damiani, E. and De Leoni, M. and Delias, P. and Van Dongen, B.F. and Dumas, M. and Dustdar, S. and Fahland, D. and Ferreira, D.R. and Gaaloul, W. and Van Geffen, F. and Goel, S. and Günther, C. and Guzzo, A. and Harmon, P. and Ter Hofstede, A. and Hoogland, J. and Ingvaldsen, J.E. and Kato, K. and Kuhn, R. and Kumar, A. and La Rosa, M. and Maggi, F. and Malerba, D. and Mans, R.S. and Manuel, A. and McCreesh, M. and Mello, P. and Mendling, J. and Montali, M. and Motahari-Nezhad, H.R. and Zur Muehlen, M. and Munoz-Gama, J. and Pontieri, L. and Ribeiro, J. and Rozinat, A. and Seguel Pérez, H. and Seguel Pérez, R. and Sepúlveda, M. and Sinur, J. and Soffer, P. and Song, M. and Sperduti, A. and Stilo, G. and Stoel, C. and Swenson, K. and Talamo, M. and Tan, W. and Turner, C. and Vanthienen, J. and Varvaressos, G. and Verbeek, E. and Verdonk, M. and Vigo, R. and Wang, J. and Weber, B. and Weidlich, M. and Weijters, T. and Wen, L. and Westergaard, M. and Wynn, M.},
title = {Process mining manifesto},
journal = {Lecture Notes in Business Information Processing},
year = {2012},
volume = {99 LNBIP},
number = {PART 1},
pages = {169-194},
doi = {10.1007/978-3-642-28108-2_19},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Lee, Y., Kim, S., & Song, M. (2012). ECM (Enterprise Content Management) System Analysis Using Process Mining. In the Korean BI Data Mining Conference, Bexco, Busan, Korea, November 30 - December 1.
@inproceedings{conf30,
author = {Lee, Y. and Kim, S. and Song, M.},
title = {ECM (Enterprise Content Management) System Analysis Using Process Mining.},
journal = {In the Korean BI Data Mining Conference, Bexco, Busan, Korea, November 30 - December 1},
year = {2012}
}
Yahya, B. N., Song, M., Bae, H., Jeon, D., Sul, S., & Asriana, R. (2012). Port Logistics Data Analysis using Process Mining. In KORMS/KIIE Co-Conference, Seoul National University of Science and Technology, Seoul, Korea, November 1.
@inproceedings{conf31,
author = {Yahya, B. N. and Song, M. and Bae, H. and Jeon, D. and Sul, S. and Asriana, R.},
title = {Port Logistics Data Analysis using Process Mining.},
journal = {In KORMS/KIIE Co-conference, Seoul National University of Science and Technology, Seoul, Korea, November 1},
year = {2012}
}
Song, M., Lee, Y., Park, M., Kim, S., & Yoo, S. (2012). Hospital Data Analysis using Process Mining. In KORMS/KIIE Co-Conference, Seoul National University of Science and Technology, Seoul, Korea, November 1.
@inproceedings{conf32,
author = {Song, M. and Lee, Y. and Park, M. and Kim, S. and Yoo, S.},
title = {Hospital Data Analysis using Process Mining.},
journal = {In KORMS/KIIE Co-conference, Seoul National University of Science and Technology, Seoul, Korea, November 1},
year = {2012}
}
Son, S., Moon, H., Kim, J., & Song, M. (2012). Trade Exhibition Data Analysis Using Process Mining. In KIIE Conference, Hanyang University ERICA Campus, Kyunggi, Korea, November 2.
@inproceedings{conf33,
author = {Son, S. and Moon, H. and Kim, J. and Song, M.},
title = {Trade Exhibition Data Analysis Using Process Mining.},
journal = {In KIIE Conference, Hanyang University ERICA Campus, Kyunggi, Korea, November 2},
year = {2012}
}
Song, M., Jung, I., Cho, Y., & Cho, H. (2012). Progress of Production Data Analysis using Process Mining. In KORMS/KIIE Conference, Hyndai Hotel, Gyeongju, Korea, May 10-11.
@inproceedings{conf35,
author = {Song, M. and Jung, I. and Cho, Y. and Cho, H.},
title = {Progress of Production Data Analysis using Process Mining.},
journal = {In KORMS/KIIE conference, Hyndai hotel, Gyeongju, Korea, May 10-11},
year = {2012}
}
Song, M., Son, S., Ryu, K., Lee, S., Cho, Y., & Cho, H. (2012). A method for gathering logs from c-MES. In the Korean Society of CAD/CAM Engineers Conference, Phoenixpark Convention Center, Pyungchang, Korea, February 1-3.
@inproceedings{conf37,
author = {Song, M. and Son, S. and Ryu, K. and Lee, S. and Cho, Y. and Cho, H.},
title = {A method for gathering logs from c-MES.},
journal = {In the Korean Society of CAD/CAM Engineers Conference, Phoenixpark Convention Center, Pyungchang, Korea, February 1-3},
year = {2012}
}
Song, M., Kim, D., Ryu, K., Cho, Y., & Cho, H. (2011). Manufacturing process analysis with process mining in c-MES. In the Korean Manufacturing System Conference, Daejeon, Korea, October 13-15.
@inproceedings{conf39,
author = {Song, M. and Kim, D. and Ryu, K. and Cho, Y. and Cho, H.},
title = {Manufacturing process analysis with process mining in c-MES.},
journal = {In the Korean Manufacturing System conference, Daejeon, Korea, October 13-15},
year = {2011}
}
Song, M., Kim, D., Ryu, K., Kim, S., Cho, Y., & Cho, H. (2011). Study on the Function of Manufacturing Process Monitoring in c-MES. In the Korean Society of Industrial Information Systems, Gyeongju, Korea, October 26-28.
@inproceedings{conf38,
author = {Song, M. and Kim, D. and Ryu, K. and Kim, S. and Cho, Y. and Cho, H.},
title = {Study on the Function of Manufacturing Process Monitoring in c-MES.},
journal = {In the Korean Society of Industrial Information Systems, Gyeongju, Korea, October 26-28},
year = {2011}
}
Woo, H.-G., & Song, M. (2011). A Structural Matching Approach to Managing Large Business Process Models. Proceedings on 41th International Conference on Computers & Industrial Engineering, University of South California, Los Angeles, October 23-25.
@inproceedings{woo2011,
author = {Woo, H-G. and Song, M.},
title = {A Structural Matching Approach to Managing Large Business Process Models},
journal = {Proceedings on 41th International Conference on Computers \& Industrial Engineering, University of South California, Los Angeles, October 23-25},
year = {2011}
}
Song, M., Kang, Y. S., C.H, & Ryu. (2010). Business process analysis using process mining technique. In the Korean Industrial Information Society, UNIST, Ulsan, Korea, May 28-29.
@inproceedings{conf40,
author = {Song, M. and Kang, Y.S. and C.H and Ryu},
title = {Business process analysis using process mining technique.},
journal = {In the Korean Industrial Information Society, UNIST, Ulsan, Korea, May 28-29},
year = {2010}
}
Van Der Aalst, W. M. P., Pesic, M., & Song, M. (2010). Beyond process mining: From the past to present and future. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6051 LNCS, 38–52.
@inproceedings{VanDerAalst201038,
author = {Van Der Aalst, W.M.P. and Pesic, M. and Song, M.},
title = {Beyond process mining: From the past to present and future},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2010},
volume = {6051 LNCS},
pages = {38-52},
doi = {10.1007/978-3-642-13094-6_5},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Koschmider, A., Song, M., & Reijers, H. A. (2009). Social software for modeling business processes. Lecture Notes in Business Information Processing, 17 LNBIP, 666–677.
@inproceedings{Koschmider2009666,
author = {Koschmider, A. and Song, M. and Reijers, H.A.},
title = {Social software for modeling business processes},
journal = {Lecture Notes in Business Information Processing},
year = {2009},
volume = {17 LNBIP},
pages = {666-677},
doi = {10.1007/978-3-642-00328-8_67},
document_type = {Conference Paper},
bpa = {Scopus},
source = {Scopus}
}
Koschmider, A., Song, M., & Reijers, H. A. (2009). Advanced social features in a recommendation system for process modeling. Lecture Notes in Business Information Processing, 21 LNBIP, 109–120.
@inproceedings{Koschmider2009109,
author = {Koschmider, A. and Song, M. and Reijers, H.A.},
title = {Advanced social features in a recommendation system for process modeling},
journal = {Lecture Notes in Business Information Processing},
year = {2009},
volume = {21 LNBIP},
pages = {109-120},
doi = {10.1007/978-3-642-01190-0_10},
document_type = {Article},
bpa = {Scopus},
source = {Scopus}
}
Mans, R. S., Schonenberg, M. H., Song, M., van der Aalst, W. M. P., & Bakker, P. J. M. (2009). Application of Process Mining in Healthcare – A Case Study in a Dutch Hospital. In A. Fred, J. Filipe, & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies. BIOSTEC 2008. Communications in Computer and Information Science (Vol. 25, pp. 425–438). Springer Berlin Heidelberg.
@inproceedings{10.1007/978-3-540-92219-3_32,
author = {Mans, R. S. and Schonenberg, M. H. and Song, M. and van der Aalst, W. M. P. and Bakker, P. J. M.},
editor = {Fred, Ana and Filipe, Joaquim and Gamboa, Hugo},
title = {Application of Process Mining in Healthcare -- A Case Study in a Dutch Hospital},
journal = {Biomedical Engineering Systems and Technologies. BIOSTEC 2008. Communications in Computer and Information Science},
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Reijers, H. A., Song, M., Romero, H., Dayal, U., Eder, J., & Koehler, J. (2009). A collaboration and productiveness analysis of the BPM community. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5701 LNCS, 1–14.
@inproceedings{Reijers20091,
author = {Reijers, H.A. and Song, M. and Romero, H. and Dayal, U. and Eder, J. and Koehler, J.},
title = {A collaboration and productiveness analysis of the BPM community},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
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Song, M., Günther, C. W., & Van Der Aalst, W. M. P. (2009). Trace clustering in process mining. Lecture Notes in Business Information Processing, 17 LNBIP, 109–120.
@inproceedings{Song2009109,
author = {Song, M. and Günther, C.W. and Van Der Aalst, W.M.P.},
title = {Trace clustering in process mining},
journal = {Lecture Notes in Business Information Processing},
year = {2009},
volume = {17 LNBIP},
pages = {109-120},
doi = {10.1007/978-3-642-00328-8_11},
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Mans, R. S., Schonenberg, M. H., Song, M., Van Der Aalst, W. M. P., & Bakker, P. J. M. (2008). Process mining in healthcare - A case study. HEALTHINF 2008 - 1st International Conference on Health Informatics, Proceedings, 1, 118–125.
@inproceedings{Mans2008118,
author = {Mans, R.S. and Schonenberg, M.H. and Song, M. and Van Der Aalst, W.M.P. and Bakker, P.J.M.},
title = {Process mining in healthcare - A case study},
journal = {HEALTHINF 2008 - 1st International Conference on Health Informatics, Proceedings},
year = {2008},
volume = {1},
pages = {118-125},
document_type = {Conference Paper},
source = {Scopus}
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Song, M., & Van Der Aalst, W. M. P. (2007). Supporting process mining by showing events at a glance. WITS 2007 - Proceedings, 17th Annual Workshop on Information Technologies and Systems, 140–145.
@inproceedings{Song2007140,
author = {Song, M. and Van Der Aalst, W.M.P.},
title = {Supporting process mining by showing events at a glance},
journal = {WITS 2007 - Proceedings, 17th Annual Workshop on Information Technologies and Systems},
year = {2007},
pages = {140-145},
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Weijters, T., van der Aalst, W. M. P., van Dongen abd C.W. Günther, B., Mans, R., de Medeiros, A. K. A., Rozinat, A., Song, M., & Verbeek, H. M. W. (2007). Process Mining with ProM. Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2007).
@inproceedings{Weijters2007,
author = {Weijters, T. and van der Aalst, W.M.P. and van Dongen abd C.W. Günther, B. and Mans, R. and de Medeiros, A.K.A. and Rozinat, A. and Song, M. and Verbeek, H.M.W.},
title = {Process Mining with ProM},
journal = {Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2007)},
year = {2007},
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Van Der Aalst, W. M. P., Van Den Brand, P. C. W., Van Dongen, B. F., Günther, C. W., Mans, R. S., Alves De Medeiros, A. K., Rozinat, A., Song, M., Verbeek, H. M. W., & Weijters, A. J. M. M. (2007). Business process analysis with ProM. WITS 2007 - Proceedings, 17th Annual Workshop on Information Technologies and Systems, 223–224.
@inproceedings{VanDerAalst2007223,
author = {Van Der Aalst, W.M.P. and Van Den Brand, P.C.W. and Van Dongen, B.F. and Günther, C.W. and Mans, R.S. and Alves De Medeiros, A.K. and Rozinat, A. and Song, M. and Verbeek, H.M.W. and Weijters, A.J.M.M.},
title = {Business process analysis with ProM},
journal = {WITS 2007 - Proceedings, 17th Annual Workshop on Information Technologies and Systems},
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pages = {223-224},
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Alves De Medeiros, A. K., Pedrinaci, C., Van Der Aalst, W. M. P., Domingue, J., Song, M., Rozinat, A., Norton, B., & Cabral, L. (2007). An outlook on Semantic business process mining and monitoring. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4806 LNCS(PART 2), 1244–1255.
@inproceedings{AlvesDeMedeiros20071244,
author = {Alves De Medeiros, A.K. and Pedrinaci, C. and Van Der Aalst, W.M.P. and Domingue, J. and Song, M. and Rozinat, A. and Norton, B. and Cabral, L.},
title = {An outlook on Semantic business process mining and monitoring},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2007},
volume = {4806 LNCS},
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pages = {1244-1255},
document_type = {Conference Paper},
source = {Scopus},
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Reijers, H. A., Song, M., & Jeong, B. (2007). On the performance of workflow processes with distributed actors: Does place matter? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4714 LNCS, 32–47.
@inproceedings{Reijers200732,
author = {Reijers, H.A. and Song, M. and Jeong, B.},
title = {On the performance of workflow processes with distributed actors: Does place matter?},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2007},
volume = {4714 LNCS},
pages = {32-47},
doi = {10.1007/978-3-540-75183-0_3},
document_type = {Conference Paper},
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Van Der Aalst, W. M. P., Van Dongen, B. F., Günther, C. W., Mans, R. S., De Alves Medeiros, A. K., Rozinat, A., Rubin, V., Song, M., Verbeek, H. M. W., & Weijters, A. J. M. M. (2007). ProM 4.0: Comprehensive support for real process analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4546 LNCS, 484–494.
@inproceedings{VanDerAalst2007484,
author = {Van Der Aalst, W.M.P. and Van Dongen, B.F. and Günther, C.W. and Mans, R.S. and De Alves Medeiros, A.K. and Rozinat, A. and Rubin, V. and Song, M. and Verbeek, H.M.W. and Weijters, A.J.M.M.},
title = {ProM 4.0: Comprehensive support for real process analysis},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2007},
volume = {4546 LNCS},
pages = {484-494},
doi = {10.1007/978-3-540-73094-1_28},
document_type = {Conference Paper},
source = {Scopus},
bpa = {Scopus}
}
Song, M., van der Aalst, W. M. P., & Choi, I. (2004). Mining Social Networks from business process log. In KORMS/KIIE Conference, Jeonbuk Univ., Jeonjoo, Korea, May 21-22.
@inproceedings{conf41,
author = {Song, M. and van der Aalst, W.M.P. and Choi, I.},
title = {Mining Social Networks from business process log.},
journal = {In KORMS/KIIE conference, Jeonbuk Univ., Jeonjoo, Korea, May 21-22},
year = {2004}
}
Van Der Aalst, W. M. P., & Song, M. (2004). Mining Social Networks: Uncovering Interaction Patterns in Business Processes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3080, 244–260.
@inproceedings{VanDerAalst2004244,
author = {Van Der Aalst, W.M.P. and Song, M.},
title = {Mining Social Networks: Uncovering Interaction Patterns in Business Processes},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2004},
volume = {3080},
pages = {244-260},
doi = {10.1007/978-3-540-25970-1_16},
document_type = {Conference Paper},
source = {Scopus},
bpa = {Scopus}
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Choi, I., Song, M., & Jung, J. (2003). A Study on Framework for Process Knowledge Management. In KORMS/KIIE Conference, Handong Univ., Pohang, Korea, May 16-17.
@inproceedings{conf42,
author = {Choi, I. and Song, M. and Jung, J.},
title = {A Study on Framework for Process Knowledge Management.},
journal = {In KORMS/KIIE conference, Handong Univ., Pohang, Korea, May 16-17},
year = {2003}
}
Choi, I., Jung, J., Song, M., & Jang, M. (2003). A Research Framework for Process Knowledge Management. Proceedings of 2003 International Conference on Computers & Industrial Engineering, San Francisco, February2-4.
@inproceedings{choi2003,
author = {Choi, I. and Jung, J. and Song, M. and Jang, M.},
title = {A Research Framework for Process Knowledge Management},
journal = {Proceedings of 2003 International Conference on Computers \& Industrial Engineering, San Francisco, February2-4},
year = {2003}
}
Choi, I., Park, C., & Song, M. (2001). Process Engineering and an XML-based Process Definition Language. 2001 International Joint Conference on Computer & Industrial Engineering and IEMS, Cocoa Beach, Florida, March 5-7.
@inproceedings{choi2001,
author = {Choi, I. and Park, C. and Song, M.},
title = {Process Engineering and an XML-based Process Definition Language.},
journal = {2001 International Joint Conference on Computer \& Industrial Engineering and IEMS, Cocoa Beach, Florida, March 5-7},
year = {2001}
}
Choi, I., Sim, G., & Song, M. (2000). Process Packaging in Knowledge Management System. In KORMS/KIIE Conference, Kyungnam Univ., Changwon, Korea, April 20-21.
@inproceedings{conf43,
author = {Choi, I. and Sim, G. and Song, M.},
title = {Process Packaging in Knowledge Management System.},
journal = {In KORMS/KIIE conference, Kyungnam Univ., Changwon, Korea, April 20-21},
year = {2000}
}