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Publications

Publications

Predictive monitoring using event logs

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

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 (accepted)

J. Kim, M. Comuzzi, M. Dumas, F.M. 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 minimise 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 (2022) “Keeping our rivers clean: information-theoretic online anomaly detection for streaming business process events,” Information Systems (accepted)

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

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, C. Cappiello and G. Meroni (2020) “On the Need for Data Quality Assessment in Blockchains”, IEEE Internet Computing (accepted)

M. Comuzzi, E. Unurjargal, and C.H. 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

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