Process-Aware AI Lab

프로세스 기반 인공지능 연구실

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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.

Major research field

process mining, machine learning, business process management, blockchain.

Desired field of research

Research Keywords and Topics

Knowledge extraction from business process event logs
Classification techniques for process predictive analytics
Streaming AI analytics for process mining
Explainable predictive analytics

Research Publications
MORE

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

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