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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 Amazon, Routledge, Kyobo

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