Publications

(2026년 5월 기준)

국내 저널 (first/corresponding author with *)

[KJ1] Kim, Y., Ko, J* (co-first)., Chae, M. S., Park, S. J., & Mun, S. (2024). Development of Artificial Intelligence Model for Evaluating XR Media Interaction Based on Background EEG Data. Journal of Broadcast Engineering, 29(3), 274-290.

국내 컨퍼런스 발표 - 논문/워크숍/포스터

[KC7] 박지훈* (학부연구생), 이영석, 조한규, 배한별, 고종현*. (2026). A Reinforcement Learning Agent for Real-Time Anomaly Detection and Repair in Event Logs. 2026 한국스마트미디어학회&한국디지털산업학회 춘계학술대회

[KC6] 피티우프레데릭탄다, 고종현, 조한규. (2026). Emotional Tone and Humanitarian Expressions Shape User Engagement in Reddit Posts. 2026 한국스마트미디어학회&한국디지털산업학회 춘계학술대회

[KC5] 하산사이얀, 고종현, 조한규. (2026). A Multi-Atlas, Multi-Architecture GNN benchmark for functional connectome-based ASD classification. 2026 한국스마트미디어학회&한국디지털산업학회 춘계학술대회

[KC4] 야쿠브 알리, 고종현, 조한규. (2026). GCN-Based Identification of Brain Regions for SZ and MDD Differentiation. 2026 한국스마트미디어학회&한국디지털산업학회 춘계학술대회

[KC3] 고종현* and M. Comuzzi. (2022). Pattern-based Reconstruction of Anomalous Traces in Business Process Event Logs. 한국경영과학회 학술대회논문집, 3785-3794.

[KC2] 고종현* and M. Comuzzi. (2021). Detecting Anomalous Treatment Processes of Outpatients in Event Logs of Electronic Medical Record System. 대한산업공학회 춘계공동학술대회 논문집, 2940-2955.

[KC1] 고종현* and M. Comuzzi. (2018). Multivariate Time Series Analysis for Gas Mixture Clustering. 대한산업공학회 춘계공동학술대회 논문집, 2533-2538.

International Journals (first/corresponding author with *)

[J16] S. Sadeghianasl, D. Fischer, M. Wynn, …, J. Ko. (Under review) PraeclarusPDQ: A Reference Architecture for Process Data Quality Management, Journal: Journal of Data and Information Quality

[J15] S. Sadeghianasl, M. Wynn, R. Andrews, W. Aalst, J. Ko. (2026, Accepted) Object-Centric Event-Data Imperfection Patterns, Journal: Information Systems

[J14] M. Comuzzi, S. Kim,J. Ko, C. Cappiello, M. Salamov, B. Pernici. (Under review) On the Impact of Low-Quality Activity Labels on Data-Driven Process Analytics, Journal: International Journal of Data Science and Analytics

[J13] S. Jeong, H. W. Ko, J.-H. Kang, J. Ko* (co-first), …, and S. Mun.(2026) Event-Driven Neuromorphic Gaze Decoding via e-Skin Electrooculography, Journal: ACS Nano

[J12] S. Jeong, J. Ko* (co-first), S. Park, J. Ha, M. S. Chae, L. Kim, S. Mun.(2026) AI-Powered Computer Interface Using Evoked Potentials for XR Biometric Authentication and Individual Neural Profiling, Journal: Biocybernetics and Biomedical Engineering

[J11] J. Ko*, M. Comuzzi, F. Maggi. (2025) Detecting and Repairing Anomaly Patterns in Business Process Event Logs, Journal: Data & Knowledge Engineering

[J10] S. Mun, S. Jeong, J. Ko, J. Kang, S. Bae, Y. Choi. (2025), Advanced AI computing enabled by 2D material-based neuromorphic devices, Journal: npj Unconventional Computing – Nature

[J9] M. Comuzzi, J. Ko, F. Maggi. (2025) A Language to Model and Simulate Data Quality Issues in Process Mining, Journal of Data and Information Quality

[J8] Gianola, A., Ko, J., Maggi, F. M., Montali, M., & Winkler, S. (2025). Approximate conformance checking: Fast computation of multi-perspective, probabilistic alignments. Information Systems, 129, 102510.

[J7] O. Yessenbayev, D. C. D. Nguyen, T. Jeong,K. J.Kang,H. R.Kim, J.Ko, ... & Comuzzi, M. (2024). Combining blockchain and IoT for safe and transparent nuclear waste management: A prototype implementation. Journal of Industrial Information Integration, 39, 100596.

[J6] S. Jeong*,J.Ko*, S. Lee,J.Kang, Y. Kim,S. Y.Park & S. Mun. (2024). Optimizing lane departure warning system towards AI-centered autonomous vehicles. Sensors, 24(8), 2505..

[J5] J. Ko*and M. Comuzzi (2023) “A Systematic Review of Anomaly Detection for Business Process Event Logs”, Business & Information Systems Engineering, 65(4), 441-462.

[J4] J. Ko*and M. Comuzzi (2021) “Keeping our rivers clean: Information-theoretic online anomaly detection for streaming business process events”, Information Systems, 101894.

[J3] J. Ko*and M. Comuzzi (2021) “Detecting anomalies in business process event logs using statistical leverage”, Information Sciences, 549, 53-67.

[J2] 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, 11(6), 1-34.

[J1] H. Nguyen, S. Lee, J. Kim, J. Koand M. Comuzzi (2019) “Autoencoders for Improving Quality of Process Event Logs”, Expert Systems with Applications, 131, 132-147.

International peer-reviewed Conferences and Workshops

[C12] Youngjin Kim, Marco Comuzzi, Moe Wynn and Jonghyeon Ko, A Predictive Process Monitoring Framework Leveraging Enabled Activities via Translucent Event Logs. ASPAI Conference (2026)

[C11] J. Ko*, M. Wynn, M. Comuzzi, F. Maggi, A Reinforcement Learning Framework for Event Log Anomaly Detection and Repair. In International Conference on Process Mining (2025)

[C10] Comuzzi, M., Kim, S., Ko, J., Salamov, M., Cappiello, C., & Pernici, B. (2024). On the Impact of Low-Quality Activity Labels in Predictive Process Monitoring. In International Conference on Process Mining (pp. 201-213).

[C9] J. Ko, F. Maggi, M. Montali, R. Penaloza, and R. pereira (2023) “Plan Recognition as Probabilistic Trace Alignment”, 5th International Conference on Process Mining (ICPM 2023).

[C8] J. Ko,A. Gianola, F. Maggi, M. Montali, and S. Winkler (2023) “Approximating Multi-Perspective Trace Alignment Using Trace Encodings”, 21stInternationalConferenceonBusinessProcessManagement(BPM2023).

[C7] J. Ko*and M. Comuzzi (2022) “Pattern-based Reconstruction of Anomalous Traces in Business Process Event Logs”, In: International Workshop on Computational Intelligence for Process Mining (CI4PM) co-located with the IEEE World Congress on Computational Intelligence (WCCI 2022), p. 18-22.

[C6] J. Ko*and M. Comuzzi (2021) “Business Process Event Log Anomaly Detection based on Statistical Leverage”, Proc. 1th ITalian forum on Business Process Management held in conjunction with BPM 2021.

[C5] J. Ko*and M. Comuzzi (2020) “Online anomaly detection using statistical leverage for streaming business process events”, In: Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers. Springer Nature, 2021. p. 193.

[C4] J. Ko*, J. Lee, and M. Comuzzi (2020) “AIR-BAGEL: An Interactive Root cause-Based Anomaly Generator for Event Logs”, 2ndInt.Conf.onProcessMining(ICPM)–DemonstrationTrack,pp.35-38

[C3] J. Kim, J. Ko, and S. Lee (2019) “Process discovery and deviation analysis of purchase order handling process”, 9thInternationalBusinessProcessIntelligenceChallenge.

[C2] M. Comuzzi, J. Ko, and S. Lee (2019) “Predicting Outpatient Process Flows to Minimise the Cost of Handling Returning Patients: A Case Study”, Proceedings of the 2nd International Workshop on Process-Oriented Data Science for Healthcare 2019 in conjunction with International Conference on Business Process Management- PODS4H19, pp 557-569.

[C1] J. Ko*and M. Comuzzi (2017) “Fuzzy Analytic Network Process for evaluating ERP post-implementation alternatives”, 2017 IEEE International Conference on Fuzzy Systems - FUZZ-IEEE 2017, pp. 1-6.