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Welcome to
Process-Aware Artificial Intelligence Lab

Professor Marco Comuzzi

About

스크린샷 2020-11-19 오전 4.50.09
  • .
  • Office: UNIST Main Campus - 112 - Building 302-2 room

Introduction of Lab

This lab was established in 2016. It focuses on the application of machine learning techniques (mainly classification) to the analysis of business process event logs. As a lab, we are always trying to contribute to the main international academic conferences in the field: Int. Conf. on Business Process Management, Int. Conf. on Process Mining and Int. Conf. on Advanced Information Systems Engineering. Our research has always practical relevance. For instance we have applied the results of our research on event logs obtained from the Ulsan Port Authority and from a large university hospital in Korea.

Research Keywords

Business process management, process mining, machine learning, classification, anomaly detection, blockchain, data quality, data science.

Research Topics

  • Predictive monitoring of business processes using event logs
  • Anomaly detection in business process execution using event log
  • Explainable business process predictive monitoring
  • Process mining maturity model
  • Quality analysis and improvement of event logs
  • Industrial and practical applications of business process mining
  • Data quality in blockchain systems

Members

Head of Lab

마르코
Marco Comuzzi
Associate professor in Department of Industrial Engineering (UNIST)
Email
mcomuzzi@unist.ac.kr
Office
UNIST Main Campus 112 - 301-9
Tel
+82 - 52-217-3187 (Fax: 3101)
Personal page
https://marco-comuzzi.github.io/
  • Since September 2017: Associate Professor of Business Process Management, Department of Industrial Engineering, UNIST, Ulsan, Korea.

Members

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김 성규 (Sungkyu Kim )
Combined MS/PhD program
Lab
UNIST Main Campus 112 - 302 - 2
Email
kimkangf3@unist.ac.kr
  • Bio info (랩장)
  • Aug 2022 – now: Combined MS/PhD program student at Industrial Engineering, UNIST (랩장)
  • March 2016 – Feb 2022: BS in Business Administration (Double Major with Mechanical Engineering, UNIST
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Astria Hijriani
PhD Program
Lab
UNIST Main Campus 112 - 302 - 2
Email
astria.hijriani@unist.ac.kr
  • Bio info
  • Feb 2022 – now: PhD program student at Industrial Engineering, UNIST
  • Magister of Computer in Information Technology, Sepuluh November Institute of Technology, Indonesia 
  • Bachelor in Computer Science, Gadjah Mada University, Indonesia
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김 연수 ( Yeonsu Kim)
MS program
Lab
UNIST Main Campus 112 - 302 - 2
Email
yeon17@unist.ac.kr
  • Bio info
  • Feb 2023 – now: Master student program at Industrial Engineering, UNIST
  • March 2017 – Feb 2022: BS in School of Business Administration, UNIST

  • Former Members

홈피사진
고종현 (Jonghyeon Ko)
Combined MS/PhD program
Lab
UNIST Main Campus 112 - 302-2
Email
whd1gus2@unist.ac.kr
  • Bio info
  • Current position: Researcher at Jeonju University
  • 2016 – 2022: Combined MS/PhD program student at Industrial Engineering (Management Engineering), UNIST
  • 2012 – 2016: BS in Statistics (Double Major with Industrial Engineering)
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김종찬 (Jongchan Kim)
Combined MS/PhD program
Email
jckim@unist.ac.kr
  • Bio info
  • Current Position: Assisstant Professor in Yonsei University
  • 2016 – 2021: Combined MS/PhD program student at Industrial Engineering (Management Engineering), UNIST
  • 2011 – 2015: BS in Management Engineering at UNIST
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권나현
MS program
Lab
UNIST Main Campus 112 - 302-2
Email
eekfskgus@unist.ac.kr
  • Bio info
  • Current Position: LG CNS
  • 2021 – 2023: Master program student at Industrial Engineering, UNIST
  • 2016 – 2020 : BS in Industrial Engineering at UNIST
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이수환 (Suhwan Lee)
MS program
Email
ghksdl6025@unist.ac.kr
  • Bio info
  • Current position: Ph.D Candidate at Universiteit of Utrecht, The Netherlands
  • 2019 – 2021: Master program student at Industrial Engineering (Management Engineering), UNIST
  • 2012 – 2018: BA in Management at UNIST
  • 2018: Research internship program at IEL lab
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Marcela Vargas Santamaria
MS program
Email
alexmirker@gmail.com
  • Bio info
  • Current position: The Ministry of Science, Innovation, Technology, and Telecommunication, Costa Rica 
  • 2017 – 2018: MS in Management Engineering at UNIST
  • 2017 – 2018: Master program student in IEL lab
  • 2012 – 2016: BBA in Marketing and International Business, Technology Management/Information Systems/Entrepreneurship and Finance/Accountiong at UNIST
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Erdenekhuu Unurjargal
MS program
Email
smile.eegii@gmail.com
  • Bio info
  • Current position: Ph.D Candidate Gakushuin University, Japan  
  • 2017 – 2018: MS in Management Engineering at UNIST
  • 2017 – 2018: Master program student in IEL lab
  • 2010 – 2014: BS in Technology Management/Information Systems/Entrepreneurship and Finance/Accountiong at UNIST
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Hoang Nguyen
MS program
Email
hoangnguyen3892@unist.ac.kr
  • Bio info
  • Current position: Trusting Social, Vietnam 
  • 2016 – 2017: MS in Management Engineering at UNIST
  • 2016 – 2017: Master program student in IEL lab
  • 2010 – 2014: BS in Corporate Finance at Danang University of Economics
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이종엽
MS program
Lab
UNIST Main Campus 112 - 302-2
Email
belllight@unist.ac.kr
  • Bio info
  • 2019 – 2022: Master program student at Industrial Engineering (Management Engineering), UNIST
  • 2014 – 2019: BS in Management Engineering at UNIST
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Gyunam Park
Undergraduate intern
Email
shot9448@unist.ac.kr
  • Bio info
  • Current position: Ph.D Candidate at RWTH Aachen University, Germany
  • 2017-2019: MS at POSTECH 
  • 2011 – 2016: BS in Management/Computer science at UNIST
  • 2016: Research internship program at IEL lab
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Minji Son
BS program
Email
limetree06@unist.ac.kr
  • Bio info
  • 2017 – 2023 : BS in Electrical & Computer Engineering at UNIST
  • 2021 : Research internship program at PAAI lab
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Jongchan Kim
BS program
Email
kimjongchan@unist.ac.kr
  • Bio info
  • 2018 – 2022 : BS in Mechanical & Nuclear Engineering at UNIST
  • 2021 : Research internship program at PAAI lab
규동
Kyudong Park
BS program
Email
kyudong0104@unist.ac.kr
  • Bio info
  • 2017 – 2023 : BS in Electrical & Computer Engineering at UNIST
  • 2021 : Research internship program at PAAI lab
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Olzhas Yessenbayev
BS program
Lab
UNIST Main Campus 112 - 302-2
Email
yess@unist.ac.kr
  • Bio info
  • 2018- 2023: BS in Industrial Engineering and Biomedical Engineering (double major) at UNIST
  • 2021: Research Internship Program at PAAI Lab
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Roman Tsoy
BS program
Lab
UNIST Main Campus 112 - 302-2
Email
romatsoy26@unist.ac.kr
  • Bio info
  • 2020-present: BS in Industrial Engineering at UNIST
  • 2021: Research internship program at PAAI Lab

Publications

Books

M. Comuzzi, P. Grefen, and G. Meroni (2023), “Blockchain for Business: IT Principles into Practice”, Taylor & Francis
Online companion website
Get it on AmazonRoutledgeKyobo

Articles in Journal and Prosiding

Predictive monitoring using event logs

S. Kim, M. Comuzzi, and C. Difrancescomarino (2023) “Understanding the impact of design choices on the performance of predictive process monitoring” Proc. 4th Int. Workshop on Leveraging Machine Learning in Process Mining (ML4PM) held in conjunction with ICPM 2023

S. Lee, M. Comuzzi, X. Lu, and H. Reijers (2023) “Measuring the Stability of Process Outcome Predictions in Online Settings” 2023 5th International Conference on Process Mining (ICPM), 105-112

N. Kwon and M. Comuzzi (2022) “Genetic algorithms for AutoML in process predictive monitoring”  Proc. 3rd  Int. Workshop on  Leveraging Machine Learning in  Process Mining ( ML 4PM) held in conjunction with ICPM 202 2,  242-254

B. Tama and M. Comuzzi (2022) “Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs” Electronics – Special issue on Predictive and Learning Control in Engineering Applications, 11(16), 2548.

J. Kim, M. Comuzzi, M. Dumas, FM Maggi, and I. Teinemaa (2022) “Encoding Resource Experience for Predictive Process Monitoring,” Decision Support Systems, 153, 113669

S. Lee, M.Comuzzi, and N. Kwon (2022) “Exploring the Suitability of Rule-Based Classification to Provide Interpretability in Outcome-Based Process Predictive Monitoring”Algorithms, 15(6), 187

J. Kim and M. Comuzzi (2021) “Stability metrics for enhancing the evaluation of outcome-based business process predictive monitoring” IEEE Access, 9, 133461-133471

S. Lee, M. Comuzzi, and X. Lu (2021) “Continuous performance evaluation for business process outcome monitoring,” Proc. 2nd Int. Workshop on Streaming Analytics for Process Mining (SA4PM) held in conjunction with ICPM 2021, 237-249

J. Kim, M. Comuzzi, M. Dumas, FM Maggi, and I. Teinemaa (2021) “Encoding Resource Experience for Predictive Process Monitoring,” Decision Support Systems (accepted)

J. Kim and M. Comuzzi (2021) “A diagnostic framework for imbalanced classification in business process predictive monitoring” Expert Systems with Applications, 184, 115536

B. Tama, M. Comuzzi and J. Ko (2020) “An empirical investigation of different classifiers, encoding and ensemble schemes for next event prediction using business process event logs”, ACM Transactions on Intelligent Systems and Technology (accepted) 

B. Tama and M. Comuzzi (2019) “An empirical comparison of classification techniques for next event prediction using business process event logs”, Expert Systems with Applications, 129, 233-245

M. Comuzzi, J. Ko and S. Lee (2019) “Predicting outpatient process flows to minimize the cost of handling returning patients: A case study”, Workshop on Process-Oriented Data Science for Healthcare (PODS4H 2019) held in conjunction with BPM 2019 (accepted)

M. Comuzzi, A.E. Marquez-Chamorro, and M. Resinas (2018) “A Hybrid Reliability Metric for SLA Predictive Monitoring”, 34th ACM Symposium on Applied Computing (accepted)

M. Comuzzi, A. Marquez-Chamorro and M. Resinas (2018) “Does your accurate process predictive monitoring model give reliable predictions?” 1st ICSOC Workshop on AI and Data Mining for Services

 

Event log anomaly detection

J. Ko and M. Comuzzi (2023), “A Systematic Review of Anomaly Detection for Business Process Event Logs” Business & Information Systems Engineering, 1-22

J. Ko and M. Comuzzi (2022) “Keeping our rivers clean: information-theoretic online anomaly detection for streaming business process events,” Information Systems, 104,101894

J. Ko and M. Comuzzi (2021) “Business Process Event Log Anomaly Detection based on Statistical Leverage”, Proc. 1st ITalian forum on Business Process Management held in conjunction with BPM 2021

J. Ko and M. Comuzzi (2021) “Detecting anomalies in business process event logs using statistical leverage”, Information Sciences, 549, 53-67

J. Ko and M. Comuzzi (2020) “Online anomaly detection using statistical leverage for streaming business process events”, 1st Workshop on Streaming Analytics for Process Mining (SA4PM) held in conjunction with ICPM 2020, 193-205

J. Ko and M. Comuzzi (2020) “Detecting anomalies in business process event logs using statistical leverage”, Information Sciences (accepted)

J. Ko, J. Lee, and M. Comuzzi (2020) “AIR-BAGEL: An Interactive Root cause-Based Anomaly Generator for Event Logs”, 2nd Int. Conf. on Process Mining (ICPM) – Demonstration Track (accepted)

H. Nguyen, S. Lee, J. Kim, J. Ko and M. Comuzzi (2019) “Autoencoders for Improving Quality of Process Event Logs”, Expert Systems with Applications, 131, 132-147

H. Nguyen and M. Comuzzi (2018) “Event log reconstruction using autoencoders”, 1st ICSOC Workshop on AI and Data Mining for Services

 

Blockchain

O. Yessanbayev, M. Comuzzi, G. Meroni, and D. Nguyen (2023) “A Middleware for Hybrid Blockchain Applications: Towards Fast, Affordable, and Accountable Integration” Proc. 21st Int. Conf. on Service-Oriented Computing (ICSOC)

M. Comuzzi, P. Grefen, and G. Meroni (2023), “Blockchain for Business: IT Principles into Practice”, Taylor & Francis (Book)

G. Meroni, M. Comuzzi, and J. Kopke (2023), “Blockchain for trusted information systems”, Frontiers in Blockchain, 6, 1235704

K. Scharer and M. Comuzzi (2023) “The quantum threat to blockchain: summary and timeline analysis”, Quantum Machine Intelligence, 5(1), 19

M. Comuzzi and P. Grefen (2023) “Blockchain for Business: Understanding Blockchain and How It Creates Business Value”, 27th International Conference on Enterprise Design, Operations Computing,(EDOC 2023)

M. Comuzzi C. Cappiello, F. Daniel, and G. Meroni (2022) “Toward Quality-Aware Transaction Validation in Blockchains”, IEEE Software, 39(4), 54-62

M. Comuzzi, C. Cappiello and G. Meroni (2021) “On the Need for Data Quality Assessment in Blockchains”, IEEE Internet Computing, 25(3), 71-78

M. Comuzzi, E. Unurjargal, and CH Lim (2018) “Towards a design space for blockchain-based system reengineering”, 1st Workshop on Blockchains for Inter-Organizational Collaboration (BIOC’18), in conjunction with CAiSE 2018, pp. 138-143

 

Others

A. Hijriani and M. Comuzzi (2024) “Towards a Maturity Model of Process Mining as an Analytic Capability” 57th Hawaii International Conference on System Sciences (HICSS)

Hofstede AH, Koschmider A, Marrella A, Andrews R, Fischer DA, Sadeghianasl S, Wynn MT, Comuzzi M, De Weerdt J, Goel K, Martin N (2023) “Process-Data Quality: The True Frontier of Process Mining”. ACM Journal of Data and Information Quality. 2023 Jul 28

Grefen, P.; Vanderfeesten, I.; Wilbik, A.; Comuzzi, M.; Ludwig, H.; Serral, E.; Kuitems, F.; Blanken, M.; Pietrasik, M. (2023) “Towards Customer Outcome Management in Smart Manufacturing.” Machines, 11, 636

G. Park, M. Comuzzi, and van der Aalst, WMP (2022) “Supporting Impact Analysis of Process-Aware Information System Updates Using Digital Twins of Organizations” Proc. 16th Int. Conf. on Research Challenges in Information Science (RCIS), pp. 158-176.

J. Munoz-Gama et al. (2022) “Process Mining for Healthcare: Characteristics and Challenges,” Journal of Biomedical Informatics (accepted)

M. Comuzzi, C. Cappiello, P. Plebani, and M. Fim (2021) “Assessing and Improving Measurability of Process Performance Indicators based on Quality of Logs “, Information Systems, 103, 101874

D. Beverungen et al. (2021) “Seven Paradoxes of Business Process Management in a Hyper-Connected World”, Business & Information Systems Engineering, 63(2): 145-156.

D. Beverungen et al. (2020) “Seven Paradoxes of Business Process Management in a Hyper-Connected World”, Business & Information Systems Engineering (accepted)

M. Vargas and M. Comuzzi (2020) “A multi-dimensional model of Enterprise Resource Planning Critical Success Factors”, Enterprise Information Systems, 14(1), 38-57.

B. Tama, M. Comuzzi, and Rhee, K.-H. (2019) “TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System”, IEEE Access, 7, 94497-94507.

M. Cho, M. Song, M. Comuzzi, and S. Yoo (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

 

For a complete of Prof. Comuzzi’s publications:   Link

Projects

XAI

Project title: Development of eXplainable AI Framework for Process Predictive Monitoring (2022. 06. 01 ~ 2025. 02. 28)

프로세스 예측 모니터링용 eXplainable AI 프레임워크 개발

  • The project’s application domain is process mining, ie, the application of data science techniques to event logs collected from information systems during the execution of business processes. The objective of process mining is to improve the efficiency and effectiveness of business processes. Within process mining, this project focuses on predictive process monitoring (PPM), which deals with the application of machine learning techniques for the prediction of aspects of interests of the future process execution, based on the historic data collected about the process execution. Within PPM, the specific focus of the project of this project is to develop new methods for (i) supporting the explainability of predictions in PPM applications and (ii) providing support to the automated generation and fine-tuning of PPM models, ie, AutoML forPPM. This research project consists of a total of three parts (years), from (1) design of the XAI framework for process prediction monitoring, (2) development of algorithms, (3) evaluation of practicality and reliability through expert panel evaluation, and implementation of integration with commercial process mining tools.

 

Photo source from (Mehdiyev and Fettke, 2020)

 


onlinePM

Project title: AI Research for Process Mining Based on Streaming Data (2021. 01. 01 ~ 2022. 12. 31)

스트리밍 데이터 기반의 프로세스 마이닝을 위한 AI 연구

  • This project aims at fulfilling the vision of developing the process navigation logic through the application of the event stream perspective on process data combined with reinforcement learning techniques to guide the process execution. The results achieved by this project will also help to integrate process predictive analytics into commercial process mining tools.

 


항만공사1

Project title : Development of a platform for process mining in port logistics (2019.05 ~ )

-프로세스 마이닝 용 항만공사 플랫폼 개발

Extended : Development of port logistics optimization service using big data-based container terminal congestion and import/export volume prediction system (2020.09 ~)

-빅데이터 기반 컨테이너 터미널 혼잡도·수출입 물동량 예측 시스템을 활용한 항만물류 최적화 서비스 개발

  • Company : Ulsan Port Authority
  • The objective of this project is to develop a platform for process mining in port logistics. While, in fact, process mining has been extensively applied in domains such as health care and public administration, applications of process mining in logistics and, particularly, port logistics, remain limited. This implies that the logistic industry is lagging behind on the potential savings and operational improvements normally accrued by the application of process mining techniques in real world settings. Therefore, a thorough analysis of the potential of process mining applied to the analysis of port logistics data is needed. This platform aims at identifying critical aspects concerning event log collection and analysis in (port) logistics, critical use cases of process mining in different reference business processes in (port) logistics, and demonstrating the effectiveness of different process mining techniques using real world port logistics data publicly available and, possibly, collected from Ulsan Port Authority (UPA). As far as process mining techniques are concerned, the focus will be mainly on predictive process monitoring techniques, an emerging branch of process mining that aims at adapting data mining techniques for predicting aspects of interests in business processes using event log data.

 

 

 


부산대

Project title : Development of a Hospital System Optimization Platform: Operating Room Scheduling and Outpatient Service Process Optimization (2018.10 ~ 2019.02)

-병원 시스템 최적화 플랫폼 개발: 수술실 스케줄러 및 최적의 외래 서비스 프로세스 개발을 중심으로

Extended : Development of a Outpatient Waiting Time Prediction Model and Improvement of Outpatient Service Processes (2019.10 ~ 2020.07)

-외래환자 대기시간 예측 모델 개발 및 외래 서비스 프로세스 개선

  • Company : Pusan National University Hospital
  • The objective of this project is to derive optimal outpatient service process and identify additional hospital system optimization issues. The concrete list is as follows : Identify outsourcing service process through process mining; Diagnosis and simulation of outpatient service processes; Derivation and verification of reduction of waiting time for medical treatment through simulation analysis; Improving the quality of event logs based on machine learning using data currently held by Pusan ​​National University Hospital and additional collected data; Development of Real-Time Waiting Time Prediction Model; Try to estimate and minimize the number of days spent in a particular patient group; Develop predictive machine learning model to predict remaining time using patient logs.

 

 


1

Project title : Blockchain-based System Engineering (2018.01 ~ 2019.12)

-블록체인 기반 시스템 구축

 

  • UNIST Research Find (Excellent research idea discovery project)
  • Although blockcain is recognised as a potentially disruptive technology for all industries, deployment of blockchain-based solutions outside finance and, specifically, cryptocurrencies and related applications, is still largely experimental. This is particularly true when blockchain is seen as a tool for engineering information exchange among a set of collaborating parties, or a “business network”. Many emerging applications are showing that there are a variety of design choices available while (re-)engineering a system using blockchain, such as the size and content of blocks, consensus rules to create new blocks, or the nature and shape of the smart contracts to be embedded in blocks. However, a scientific approach to exhaustively identify these design choices and guide designers during implementations is currently lacking.
    This project aims at filling this gap, by devising a methodology for reengineering systems using the blockchain technology. This methodology will comprise an analysis of the blockchain-based system design space, ie., the set of design choices available to designers, and a set of guidelines to support designers of a system during implementation.

 

 

 


인터블록체인

Project title : Blockchain Platform with Business Models towards Cross-domain Interoperability (2018.06 ~ 2020.02)

-크로스 도메인 호환성을 위한 블록체인 플랫폼 및 비즈니스 개발

 

  • IBRC POSTECH (대학ICT연구센터육성지원사업)
  • The Intelligent Enterprise Lab at UNIST, chaired by Prof Marco Comuzzi, conducts research in the areas of enterprise systems, business process management and blockchain-based systems design. The Lab has received a seed funding from UNIST for the year 2018 to develop a methodology for blockchain-based system design using case studies of real blockchain-based application development in the UNIST campus. The expertise developed within this seed funding research will constitute fundamental background knowledge for the research to be conducted in this project. In particular, the developed methodology will be extended to the case of interoperability of multiple blockchains. The Lab research in business process management also contributes to build important background knowledge for this project. Prof Comuzzi has a strong track record at UNIST and his previous research in the area of cross-organisational business process management modelling and enactment. This project represents an opportunity to transfer this knowledge into the context of blcockhain-based system interoperability design and enactment.

 

 

 


태성

Project title : Development of Data Analytics Methods to Identify the Sources of Oder (2017.10 ~ 2018.03)

-악취 원인 규명을 위한 데이터 분석 기법 연구

 

  • Company : TAE SUNG ENVIRONMENT INSTITUTE
  • The project objectives of this project are to be compressed into the following three. (1) Establish a big data integration platform related to odor. (2) Development of artificial intelligence for odor analysis. (3) Design of Ulsan Metropolitan City Odor Monitoring Service System based on Artificial Intelligence. Among them, the pure research goal excluding the platform and the design part of the system is related to the second business objective ‘deodorization artificial intelligence development’. Specifically, it is necessary to develop a high-performance odor recognition artificial intelligence model by identifying and solving various problems arising in the data collection-processing-analysis stage through the repeated experiment process of applying and testing various algorithms on odor data. It is a goal.

 

 

 


이벤트로그

Project title : Development of techniques for improving quality of event logs for process mining (2017.09 ~ 2019.08)

-프로세스 마이닝 용 이벤트로그 품질 제고 기술 개발

 

  • National Research Fund
  • The objective of this project is to design and implement new techniques to improve the quality of process event logs and to study how they impact the quality of process mining outcomes.

Year 1: To revise the literature and develop preliminary models of event log cleaning and imputation techniques.

Year 2: To develop event log cleaning and imputation techniques and assess their impact on the quality of process mining outcomes.

Year 3: To evaluate the developed techniques in real world cases and refine them using the feedback collected from practitioners.

 

 

 



Project title: Techniques and tools for controlled change management in ERP post-implementation (2016. 04. 01 ~ 2017. 09. 30)

  • The purpose of this research project is to develop techniques for the controlled change management of modifications of ERP systems in the post-implementation phase. The content is as follows : Develop a genetic conceptual model of ERP systems to determine the dependencies among the different components consituting the system, introduce a taxonomy of possible post-implementation modifications of ERP systems, Define a methodology to assess the impact of different types of change, by considering in particular the ripple effects implied by specifice dependencies, Define metrics to estimate the depth of impact ERP post-implementation change, possibly based on the strategy selected to implementation the identified change. Implement a software tool, i. e., a decision support system, embodying the identified models, methods and metrics to support business analysts in the controlled management of ERP post-implementation change.

 

 

 


Teaching

Just My Classes

Business Process Management

& Process Mining
After attending this course you should have achieved the following objectives:

  • Know the importance and relevance of Business Process Management (BPM) in modern organisations;
  • Know the phases of the BPM lifecycle;
  • Can identify the relevant processes to be considered in a BPM initiative
  • Can model a business process using BPMN (Business Process Modelling Notation)
  • Can analyse and redesign a business process to improve efficiency, possibly using simulation techniques
  • Know how business processes can be automated
  • Know the importance and role of ERP system to support organizational operations and business processes
  • Know the importance of Process Mining as a technique to discover and analyse business process
  • Can discover and analyse processes mined from existing enterprise systems event logs

Business Process Management

& Process Mining
After attending this course you should have achieved the following objectives:

  • Know the importance and relevance of Business Process Management (BPM) in modern organisations;
  • Know the phases of the BPM lifecycle;
  • Can identify the relevant processes to be considered in a BPM initiative
  • Can model a business process using BPMN (Business Process Modelling Notation)
  • Can analyse and redesign a business process to improve efficiency, possibly using simulation techniques
  • Know how business processes can be automated
  • Know the importance and role of ERP system to support organizational operations and business processes
  • Know the importance of Process Mining as a technique to discover and analyse business process
  • Can discover and analyse processes mined from existing enterprise systems event logs

Applied Programming for Management Engineering

The main aim of this course is to understand how a computer can store and process data effectively. To do this, data structures and algorithms must be studied with the help of a programming language. In this course, we will use Python as a programming language.

So, besides becoming an expert about data structures and algorithms, a (positive) side effect of this course is that you will learn a new programming language, i.e., Python (which, by the way, is used extensively in Data Science and scientific programming)

After attending this course you should have achieved the following objectives:

  • Understand growth rate of algorithms using the Big-Oh notation
  • Understand how recursion can be used to design algorithms
  • Understand the principles and usage application of basic data structures, such as arrays, stack, queues and lists.
  • Understand the principles and usage applications of advanced data structures, such as trees and hash tables
  • Understand and apply typical sorting and searching algorithms
  • Understand graphs, graph traversal and paths within graphs
  • Implement basic and advanced data structures in Python
  • Implement the algorithms covered by this course in Python
  • Design and evaluate efficient algorithms in Python to solve specific applied problems
  • Understand and utilise Python libraries to solve applied problems, such as using APIs (Twitter and Google maps are used as examples), creating and processing graphs, data science with Pandas.

Data-Driven Process Management

The objective of this course is to discuss the fundamentals of modeling, analyzing and improving business processes in organizations using the data produced by their execution. The first part of the course covers the basics of business process management (modeling, simulation, improvement); the second part of the course covers the basics of “process mining”, i.e., a collection of techniques to analyze process data.

Blockchain-based system engineering

The objective of this course is to cover the basics of blockchain technology as a technology for designing and implementing cross-organisational information systems. The course starts with an overview of blockchain technology and its emergence in the field of cryptocurrency and then will focus more extensively on designing systems using blockchain.
Particular attention will be given to Bitcoin (as the first and prominent example of public blockchain), Ethereum (for smart contracts), and Hyperledger (as an example of permissioned business blockchain).

The course requires some basic knowledge of programming (Python is preferable). The level achieved with the course “Applied Programming for ME” will be more than sufficient.

After attending this course you should have achieved the following objectives:

  • Know the basics of blockchain technology
  • Know how blockchain can be used to implement cryptocurrency
  • Understand how different cryptography tools (hashing, digital signatures) can be used to implement blockchain
  • Can understand how public blockchains (e.g. Ethereum) can be used to design open systems
  • Can understand how private (permissioned) blockchains can be used to design trustful multiparty systems
  • Knows the basics of different framework to implement blochain-based systems (e.g., Ethereum, Hyperledger)
  • Can understand the importance of blockchain in different real world case studies.

Process Predictive Monitoring (Special Topics in IE - 1)

This course covers the fundamentals of predictive monitoring using business process event logs. The course covers mainly the design and implementation of business process predictive monitoring models built using machine learning techniques. As such, it focuses on topics such as feature extraction and engineering from event logs and encoding of event log information. In the second part, the course also discusses techniques for anomaly detection in event logs.

(Unedited!) recordings of this course lectures in 2020 are available at: Click Here

Thank you

Jonghyeon Ko
Jongchan Kim
Soohwan Lee
Jongyub Lee
Kubanychbekov Murat
Marco Comuzzi