The latest publications can be found via this link. 

Journal Publications

  • ​Mitrovic S., Baesens B., Lemahieu W., De Weerdt J. (2018). On the operational efficiency of different feature types for telco Churn prediction. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 267 (3), 1141-1155. doi: 10.1016/j.ejor.2017.12.015. (Impact factor: 3.30).
  • De Smedt J., De Weerdt J., Serral E., Vanthienen J. (2018). Discovering hidden dependencies in constraint-based declarative process models for improving understandability. Information Systems, 74, 40-52. doi: 10.1016/j.is.2018.01.001. (citations: 0) (Impact factor: 2.78).
 
  • Broucke SKLMV., De Weerdt J. (2017). Fodina: A robust and flexible heuristic process discovery technique. DECISION SUPPORT SYSTEMS, 100, 109-118. doi: 10.1016/j.dss.2017.04.005. (Impact factor: 3.22).
  • De Koninck P., De Weerdt J., vanden Broucke SKLM. (2017). Explaining clusterings of process instances. DATA MINING AND KNOWLEDGE DISCOVERY, 31 (3), 774-808. doi: 10.1007/s10618-016-0488-4. (Impact factor: 3.16).
  • Low WZ., van der Aalst WMP., ter Hofstede AHM., Wynn MT., De Weerdt J. (2017). Change visualisation: Analysing the resource and timing differences between two event logs. INFORMATION SYSTEMS, 65, 106-123. doi: 10.1016/j.is.2016.10.005. (citations: 1) (Impact factor: 2.78).
 
  • Low WZ., vanden Broucke SKLM., Wynn MT., ter Hofstede AHM., De Weerdt J., van der Aalst WMP. (2016). Revising history for cost-informed process improvement. COMPUTING, 98 (9), 895-921. doi: 10.1007/s00607-015-0478-1. (citations: 1) (Impact factor: 1.59).
  • Sedrakyan G., De Weerdt J., Snoeck M. (2016). Process-mining enabled feedback: "Tell me what I did wrong" vs. "tell me how to do it right". COMPUTERS IN HUMAN BEHAVIOR, 57, 352-376. doi: 10.1016/j.chb.2015.12.040. (citations: 5) (Impact factor: 3.44).
  • De Smedt J., De Weerdt J., Vanthienen J., Poels G. (2016). Mixed-Paradigm Process Modeling with Intertwined State Spaces (vol 58, pg 19, 2016). BUSINESS & INFORMATION SYSTEMS ENGINEERING, 58 (1), 101-104. doi: 10.1007/s12599-015-0421-1. (Impact factor: 3.39).
  • De Smedt J., De Weerdt J., Vanthienen J., Poels G. (2016). Mixed-Paradigm Process Modeling with Intertwined State Spaces. BUSINESS & INFORMATION SYSTEMS ENGINEERING, 58 (1), 19-29. doi: 10.1007/s12599-015-0416-y. (citations: 6) (Impact factor: 3.39).
 
  • De Smedt J., De Weerdt J., Vanthienen J. (2015). Fusion Miner: Process discovery for mixed-paradigm models. DECISION SUPPORT SYSTEMS, 77, 123-136. doi: 10.1016/j.dss.2015.06.002. (citations: 2) (Impact factor: 3.22).
  • Caron F., Vanthienen J., Vanhaecht K., Van Limbergen E., De Weerdt J., Baesens B. (2015). A process mining based investigation of adverse events in care processes. Health Information Management Journal, 43 (1), 16-25. doi: 10.12826/18333575.2013.0013.Caron. (Impact factor: 0.78).
 
  • Sedrakyan G., Snoeck M., De Weerdt J. (2014). Process mining analysis of conceptual modeling behavior of novices - empirical study using JMermaid modeling and experimental logging environment. COMPUTERS IN HUMAN BEHAVIOR, 41, 486-503. doi: 10.1016/j.chb.2014.09.054. (citations: 7) (Impact factor: 3.44).
  • Garcia-Banuelos L., Dumas M., La Rosa M., De Weerdt J., Ekanayake CC. (2014). Controlled automated discovery of collections of business process models. INFORMATION SYSTEMS, 46, 85-101. doi: 10.1016/j.is.2014.04.006. (citations: 6) (Impact factor: 2.78).
  • vanden Broucke SKLM., De Weerdt J., Vanthienen J., Baesens B. (2014). Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 26 (8), 1877-1889. doi: 10.1109/TKDE.2013.130. (citations: 15) (Impact factor: 3.44).
  • Caron F., Vanthienen J., Vanhaecht K., Van Limbergen E., De Weerdt J., Baesens B. (2014). Monitoring care processes in the gynecologic oncology department. COMPUTERS IN BIOLOGY AND MEDICINE, 44, 88-96. doi: 10.1016/j.compbiomed.2013.10.015. (citations: 11) (Impact factor: 1.84).
  • Sedrakyan G., Snoeck M., De Weerdt J. (2014). Process mining analysis of conceptual modeling behavior of novices. Computers in Human Behavior, 41, 486-503. (Impact factor: 3.44).
  • Van Molle E., Vanderloock A., De Weerdt J., Lemahieu W., Sels L., Baesens B., Klewais E., Bouckaert D. (2014). Efficiëntere loopbaanbegeleiding. Informatie 49-53. (professional oriented).
 
  • De Weerdt J., Broucke SV., Vanthienen J., Baesens B. (2013). Active Trace Clustering for Improved Process Discovery. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 25 (12), 2708-2720. doi: 10.1109/TKDE.2013.64. (citations: 21) (Impact factor: 3.44).
  • De Weerdt J., Schupp A., Vanderloock A., Baesens B. (2013). Process mining for the multi-faceted analysis of business processes - A case study in a financial services organization. Computers in Industry, 64 (1), 57-67. doi: 10.1016/j.compind.2012.09.010. (Impact factor: 2.69).
 
  • De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2012). A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems, 37 (7), 654-676. doi: 10.1016/j.is.2012.02.004. (Impact factor: 2.78).
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  • Goedertier S., De Weerdt J., Martens D., Vanthienen J., Baesens B. (2011). Process discovery in event logs: An application in the telecom industry. APPLIED SOFT COMPUTING, 11 (2), 1697-1710. doi: 10.1016/j.asoc.2010.04.025. (citations: 24) (Impact factor: 3.54).
  • De Weerdt J., Schupp A., Vanderloock A., Baesens B. (2011). Datagedreven analyseren van bedrijfsprocessen op basis van process mining. Informatie, 53 (6), 34-39. (professional oriented).

Conference and Workshop Proceedings

  • Deeva G., De Smedt J., De Koninck P., De Weerdt J. (2018). Dropout prediction in MOOCs: A comparison between process and sequence mining. In: Lecture Notes in Business Information Processing: vol. 308 (pp.243-255). ISBN: 9783319740294. doi: 10.1007/978-3-319-74030-0_18. (citations: 0).
  • De Koninck P., De Weerdt J. (2017). Multi-objective trace clustering: Finding more balanced solutions. In: Lecture Notes in Business Information Processing: vol. 281 (pp.49-60). ISBN: 9783319584560. doi: 10.1007/978-3-319-58457-7_4. (citations: 0).
  • De Koninck P., Nelissen K., Baesens B., vanden Broucke S., Snoeck M., De Weerdt J. (2017). An Approach for Incorporating Expert Knowledge in Trace Clustering. In: Dubois E., Pohl K. (Eds.), ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017): vol. 10253 (pp.561-576). ISBN: 978-3-319-59535-1. doi: 10.1007/978-3-319-59536-8_35.
  • De Koninck P., De Weerdt J. (2017). Similarity-based approaches for determining the number of trace clusters in process discovery. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): vol. 10470 LNCS (pp.19-42). ISBN: 9783662558614. doi: 10.1007/978-3-662-55862-1_2. (citations: 0).
  • De Smedt J., Deeva G., De Weerdt J. (2017). Behavioral Constraint Template-Based Sequence Classification. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): vol. 10535 LNAI (pp.20-36). ISBN: 9783319712451. doi: 10.1007/978-3-319-71246-8_2. (citations: 0).
  • De Koninck P., De Weerdt J. (2017). Multi-objective trace clustering: finding more balanced solutions. In: Dumas M., Fantinato M. (Eds.), Business Process Management Workshops: BPM 2016 International Workshops: vol. 281 (pp.49-60). ISBN: 978-3-319-58456-0. doi: 10.1007/978-3-319-58457-7.
  • Deeva G., De Smedt J., De Koninck P., De Weerdt J. (2017). Dropout prediction in MOOCs: a comparison between process and sequence mining. In: Proceedings of the BPM 2017 Workshops: vol. 308 (Paper No. 7) (pp.243-255). ISBN: 978-3-319-74029-4. doi: 10.1007/978-3-319-74030-0.
  • De Smedt J., De Weerdt J., Serral E., Vanthienen J. (2016). Improving understandability of declarative process models by revealing hidden dependencies. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): vol. 9694 (pp.83-98). ISBN: 9783319396958. doi: 10.1007/978-3-319-39696-56. (citations: 3).
  • De Koninck P., De Weerdt J. (2016). Determining the number of trace clusters: A Stability-based approach. In: CEUR Workshop Proceedings: vol. 1592 (pp.1-15). (citations: 2).
  • Serral E., De Weerdt J., Sedrakyan G., Snoeck M. (2016). Automating Immediate and Personalized Feedback Taking Conceptual Modelling Education to a Next Level. In: Espana S., Ralyte J., Souveyet C. (Eds.), 2016 IEEE TENTH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE (RCIS) (pp.439-+).
  • van Dongen B., Ferreira DR., De Weerdt J., Burattin A. (2016). Introduction to the 11th International Workshop on Business Process Intelligence (BPI 2015). In: Reichert M., Reijers HA. (Eds.), BUSINESS PROCESS MANAGEMENT WORKSHOPS, (BPM 2015): vol. 256 (pp.110-111). ISBN: 978-3-319-42886-4.
  • De Koninck P., De Weerdt J. (2016). A Stability Assessment Framework for Process Discovery Techniques. In: LaRosa M., Loos P., Pastor O. (Eds.), BUSINESS PROCESS MANAGEMENT, BPM 2016: vol. 9850 (pp.57-72). ISBN: 978-3-319-45347-7. doi: 10.1007/978-3-319-45348-4_4.
  • De Smedt J., De Weerdt J., Serral E., Vanthienen J. (2016). Improving Understandability of Declarative Process Models by Revealing Hidden Dependencies. In: Nurcan S., Soffer P., Bajec M., Eder J. (Eds.), ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2016): vol. 9694 (pp.83-98). ISBN: 978-3-319-39695-8. doi: 10.1007/978-3-319-39696-5_6. (citations: 2).
  • De Smedt J., De Weerdt J., Serral E., Vanthienen J. (2016). Gamification of Declarative Process Models for Learning and Model Verification. In: Reichert M., Reijers HA. (Eds.), BUSINESS PROCESS MANAGEMENT WORKSHOPS, (BPM 2015): vol. 256 (pp.432-443). ISBN: 978-3-319-42886-4. doi: 10.1007/978-3-319-42887-1_35.
  • De Weerdt J., vanden Broucke SKLM., Caron F. (2015). Bidimensional Process Discovery for Mining BPMN Models. In: Fournier F., Mendling J. (Eds.), BUSINESS PROCESS MANAGEMENT WORKSHOPS( BPM 2014): vol. 202 (pp.529-540). ISBN: 978-3-319-15894-5. doi: 10.1007/978-3-319-15895-2_45. (citations: 1).
  • De Weerdt J., vanden Broucke S. (2014). SECPI: Searching for Explanations for Clustered Process Instances. In: Sadiq S., Soffer P., Volzer H. (Eds.), BUSINESS PROCESS MANAGEMENT, BPM 2014: vol. 8659 (pp.408-415). ISBN: 978-3-319-10171-2. (citations: 1).
  • van Dongen BF., Weber B., Ferreira DR., De Weerdt J. (2014). Report: Business process intelligence challenge 2013. In: Lecture Notes in Business Information Processing: vol. 171 (pp.79-87). ISBN: 9783319062563. doi: 10.1007/978-3-319-06257-0_7. (citations: 1).
  • De Smedt J., De Weerdt J., Vanthienen J. (2014). Multi-paradigm Process Mining: Retrieving Better Models by Combining Rules and Sequences. In: Meersman R., Panetto H., Dillon T., Missikoff M., Liu L., Pastor O., Cuzzocrea A., Sellis T. (Eds.), ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2014 CONFERENCES: vol. 8841 (pp.446-453). ISBN: 978-3-662-45562-3. (citations: 2).
  • van Dongen BF., Weber B., Ferreira DR., de Weerdt J. (2014). Report: Business process intelligence challenge 2013. In: Lecture Notes in Business Information Processing: vol. 171 LNBIP (pp.79-87). ISBN: 9783319062563. doi: 10.1007/978-3-319-06257-0. (citations: 2).
  • De Weerdt J., Caron F., Vanthienen J., Baesens B. (2013). Getting a grasp on clinical pathway data: An approach based on process mining. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): vol. 7769 LNAI (pp.22-35). ISBN: 9783642367779. doi: 10.1007/978-3-642-36778-6_3. (citations: 6).
  • Van Dongen BF., Weber B., Ferreira DR., De Weerdt J. (2013). Business process intelligence challenge 2013. In: CEUR Workshop Proceedings: vol. 1052 (citations: 0).
  • Van Dongen B., Weber B., Ferreira D., De Weerdt J. (2013). Preface to business process intelligence challenge 2013. In: CEUR Workshop Proceedings: vol. 1052 (citations: 0).
  • Caron F., Vanthienen J., De Weerdt J., Baesens B. (2012). Advanced care-flow mining and analysis. In: Daniel F., Barkaoui K., Dustdar S. (Eds.), Lecture Notes in Business Information Processing: vol. 99 (pp.167-168). Heidelberg. ISBN: 978-3-642-28107-5.
  • vanden Broucke S., De Weerdt J., Baesens B., Vanthienen J. (2012). An improved artificial negative event generator to enhance process event logs. In: Ralyt J., Franch X., Brinkkemper S., Wrycza S. (Eds.), Lecture Notes in Computer Science (pp.254-269). ISBN: 978-3-642-31094-2. (VABB-5).
  • De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2010). A critical evaluation study of model-log metrics in process discovery. In: zur Muehlen M., Su J. (Eds.), Business Process Management Workshops: vol. 66 (pp.158-169). ISBN: 978-3-642-20510-1.
  • Mitrovic S., Baesens B., Lemahieu W., De Weerdt J. (2017). Churn prediction using dynamic rfm-augmented node2vec. In: Proceedings of the Third international workshop on Dynamics in and of Networks, ECML-PKDD 2017.