Faculty Research Profile
Comuzzi Marco
학력
· Member of IEEE, ACM, IEEE TC on Enterprise Systems
· Service-Oriented computing and Applications (Springer) - Associate Editor
주요 경력
· 2019~Present: Director, UNIST Blockchain Research Center
· 2017~Present: Associate Professor, UNIST
· 2016~2017: Assistant Professor, UNIST
· 2013~2016: Assistant Professor, City University of London
· 2009~2013: Assistant Professor, Eindhoven University of Technology
수상/학회/외부활동
· Member of IEEE, ACM, IEEE TC on Enterprise Systems
· Service-Oriented computing and Applications (Springer) - Associate Editor
프로세스 기반 인공지능 연구실
Process-Aware AI Lab
PAAI (프로세스 기반 인공지능 연구실) 랩은 효율적이고 효과적인 "증거 기반"비즈니스 프로세스 관리를위한 솔루션 엔지니어링에 중점을 두고, 이를 위해 프로세스 이벤트 로그, 즉 비즈니스 프로세스 내에서 태스크 및 의사 결정의 "증거"를 포함하는 정보 시스템의 로그로 작업합니다. 특히, 고급 기계 학습 기술을 적용하여 프로세스 이벤트 로그에서 지식을 추출하고이 지식을 사용하여 프로세스 실행을 개선하고 있습니다. 최근에는 협업 당사자 네트워크에서 정보 교환 및 프로세스 관리를 리엔지니어링하는 도구로 블록 체인에 역점을 두고 있으며, 특히 트랜잭션 페이로드의 데이터 품질 평가를 지원하기 위해 블록 체인 시스템 (공개 및 허가 된)을 확장하는데 집중하고 있습니다.
The Process-Aware AI Lab (PAAI) Lab focuses on engineering of solutions for efficient and effective "evidence-based" business process management. To do so, we work with process event logs, i.e., logs of information systems that contain "evidence" of the execution of tasks and decisions within a business process. In particular, we apply advanced machine learning techniques to extract knowledge from process event logs and use this knowledge to improve process execution. Recently, we are also interested in blockchain as a tool for re-engineering information exchange and process management across networks of collaborating parties. Specifically, we focus on extending blockchain systems (public and permissioned) to support the assessment of data quality of transaction payloads.
The Process-Aware AI Lab (PAAI) Lab focuses on engineering of solutions for efficient and effective "evidence-based" business process management. To do so, we work with process event logs, i.e., logs of information systems that contain "evidence" of the execution of tasks and decisions within a business process. In particular, we apply advanced machine learning techniques to extract knowledge from process event logs and use this knowledge to improve process execution. Recently, we are also interested in blockchain as a tool for re-engineering information exchange and process management across networks of collaborating parties. Specifically, we focus on extending blockchain systems (public and permissioned) to support the assessment of data quality of transaction payloads.
연구분야
Process mining, Machine learning, Business process management, Blockchain
Process mining, Machine learning, Business process management, Blockchain
연구주제
· Knowledge extraction from business process event logs
· Classification techniques for process predictive analytics
· Streaming AI analytics for process mining
· Explainable predictive analytics
· Knowledge extraction from business process event logs
· Classification techniques for process predictive analytics
· Streaming AI analytics for process mining
· Explainable predictive analytics
국가연구개발사업 기술 분류체계
국가과학기술표준분류
SC. 경제/경영 > SC11. 경영관리/e-비즈니스 > SC1106. 지능형정보기술
논문
· INFORMATION SCIENCES / Detecting anomalies in business process event logs using statistical leverage / Ko, Jonghyeon; Comuzzi, Marco / 2021-03
· ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY / An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs / Tama, Bayu Adhi; Comuzzi, Marco; Ko, Jonghyeon / 2020-11
· EXPERT SYSTEMS WITH APPLICATIONS / Autoencoders for improving quality of process event logs / Nguyen, Hoang Thi Cam; Lee, Suhwan; Kim, Jongchan; Ko, Jonghyeon; Comuzzi, Marco / 2019-10
· INFORMATION SCIENCES / Optimal directed hypergraph traversal with ant-colony optimisation / Comuzzi, Marco / 2019-01
· DECISION SUPPORT SYSTEMS / Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques / Cho, Minsu; Song, Minseok; Comuzzi, Marco; Yoo, Sooyoung / 2017-12