Publications in 2017

Arriagada-Benítez, M., Sepúlveda, M., Munoz-Gama, J. & Buijs, J.C.A.M. (2017). Strategies to automatically derive a process model from a configurable process model based on event data. Applied Sciences, 7(10):1023.

Bolt, A., de Leoni, M. & van der Aalst, W.M.P. (2017). Process variant comparison: using event logs to detect differences in behavior and business rules. Information Systems,

Cheng, L., Tachmazidis, I., Kotoulas, S. & Antoniou, G. (2017). Design and evaluation of small-large outer joins in cloud computing environments. Journal of Parallel and Distributed Computing, 110, 2-15.

Cheng, L., Kotoulas, S., Ward, T.E. & Theodoropoulos, G. (2017). Improving the robustness and performance of parallel joins over distributed systems. Journal of Parallel and Distributed Computing, 109, 310-323.

de Leoni, M. & Marrella, A. (2017). Aligning real process executions and prescriptive process models through automated planning. Expert Systems with Applications, 82, 162–183.

de Leoni, M., Lanciano, G. & Marrella, A. (2017). A tool for aligning event logs and prescriptive process models through automated planning. CEUR Workshop Proceedings, 1920.

González-López de Murillas, E., Fabra, J., Álvarez, P. & Ezpeleta, J. (2017). Parallel computation of the reachability graph of petri net models with semantic information. Software : Practice and Experience, 47(5), 647-668.

Hassani, M., Töws, D., Cuzzocrea, A. & Seidl, T. (2017). BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. International Journal of Data Science and Analytics,

Hassani, M. & Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4(3), 171–183-171–183.

Kalenkova, A.A., van der Aalst, W.M.P., Lomazova, I.A. & Rubin, V.A. (2017). Process mining using BPMN: relating event logs and process models. Software and Systems Modeling, 16(4), 1019-1048.

Lee, W.L.J., Verbeek, H.M.W., Munoz-Gama, J., van der Aalst, W.M.P. & Sepúlveda, M. (2017). Replay using recomposition : alignment-based conformance checking in the large. CEUR Workshop Proceedings, 1920:157.

Li, G. & van der Aalst, W.M.P. (2017). A framework for detecting deviations in complex event logs. Intelligent Data Analysis, 21(4), 759-779.

Low, W.Z., van der Aalst, W.M.P., ter Hofstede, A.H.M., Wynn, M.T. & De Weerdt, J. (2017). Change visualisation : analysing the resource and timing differences between two event logs. Information Systems, 65, 106-123.

Mitsyuk, A.A., Shugurov, I.S., Kalenkova, A.A. & van der Aalst, W.M.P. (2017). Generating event logs for high-level process models. Simulation Modelling Practice and Theory, 74, 1-16.

Pika, A., Leyer, M., Wynn, M.T., Fidge, C.J., Ter Hofstede, A.H.M. & Van Der Aalst, W.M.P. (2017). Mining resource profiles from event logs. ACM Transactions on Management Information Systems, 8(1):1.

Polyvyanyy, A., Ouyang, C., Barros, A. & van der Aalst, W.M.P. (2017). Process querying : enabling business intelligence through query-based process analytics. Decision Support Systems, 100, 41-56.

Poppe, E., Wynn, M. T., Ter Hofstede, A.H.M., Brown, R., Pini, A. & Van Der Aalst, W.M.P. (2017). Processprofiler3D : A tool for visualising performance differences between process cohorts and process instances. CEUR Workshop Proceedings, 1920:203

Raichelson, L., Soffer, P. & Verbeek, H.M.W. (2017). Merging event logs : combining granularity levels for process flow analysis. Information Systems, 71, 211-227.

Reijers, H.A., Vanderfeesten, I.T.P., Plomp, M.G.A., Van Gorp, P.M.E., Fahland, D., van de Crommert, W.L.M. & Diaz Garcia, H.D. (2017). Evaluating data-centric process approaches: does the human factor factor in?. Software and Systems Modeling, 16(3), 649-662.

Schueller, D., Beecks, C., Hassani, M., Hinnell, J., Brenger, B., Seidl, T. & Mittelberg, I. (2017). Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action. Digital Humanities Quarterly, 11(2), 1-14.

SteadieSeifi, M., Dellaert, N. P., Nuijten, W. & Van Woensel, T. (2017). A metaheuristic for the multimodal network flow problem with product quality preservation and empty repositioning. Transportation Research. Part B: Methodological, 106, 321-344.

Suriadi, S., Wynn, M.T., Xu, J., van der Aalst, W.M.P. & ter Hofstede, A.H.M. (2017). Discovering work prioritisation patterns from event logs. Decision Support Systems, 100, 77-92.

van der Aa, H., Leopold, H. & Reijers, Hajo A. (2017). Comparing textual descriptions to process models : the automatic detection of inconsistencies. Information Systems, 64, 447-460.

van der Aa, H., Leopold, H., del-Río-Ortega, A., Resinas, M. & Reijers, H.A. (2017). Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models. Information Systems, 71, 27-39.

van der Aalst, W.M.P., Artale, A., Montali, M. & Tritini, S. (2017). Object-centric behavioral constraints : Integrating data and declarative process modelling. CEUR Workshop Proceedings, 1879:51.

van Eck, M.L., Firat, M., Nuijten, W.P.M., Sidorova, N. & van der Aalst, W.M.P. (2017). Human performance-aware scheduling and routing of a multi-skilled workforce. Complex Systems Informatics and Modeling Quarterly. (12), 1-21.

van Hulst, D., den Hertog, D. & Nuijten, W.P.M. (2017). Robust shift generation in workforce planning. Computational Management Science, 14(1), 115-134.

van Zelst, S.J., Bolt Irondio, A.J., Hassani, M., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Online conformance checking: relating event streams to process models using prefix-alignments. International Journal of Data Science and Analytics.

van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P. & Verbeek, H.M.W. (2017). Discovering workflow nets using integer linear programming. Computing.

van Zelst, S.J., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Event stream-based process discovery using abstract representations. Knowledge and Information Systems, 1-29.

Verbeek, H.M.W., Munoz Gama, J. & van der Aalst, W.M.P. (2017). Divide and conquer: a tool framework for supporting decomposed discovery in process mining. The Computer Journal, 60(11), 1649-1674.

Wynn, M.T., Poppe, E., Xu, J., ter Hofstede, A.H.M., Brown, R., Pini, A. & van der Aalst, W.M.P. (2017). ProcessProfiler3D : a visualisation framework for log-based process performance comparison. Decision Support Systems, 100, 93-108.

Brochenin, R., Buijs, J.C.A.M., Vahdat, M. & van der Aalst, W.M.P. (2017). Resource usage analysis from a different perspective on MOOC dropout. arXiv, (1710.05917v1),

Leemans, M., van der Aalst, W.M.P. & van den Brand, M.G.J. (2017). Recursion aware modeling and discovery for hierarchical software event log analysis (extended). arXiv:1710.09323v1

Bichler, M., Heinzl, A. & van der Aalst, W.M.P. (2017). Business analytics and data science : once again?. Business & Information Systems Engineering : The International Journal of WIRTSCHAFTSINFORMATIK, 59(2), 77-79.

Heinzl, A., Bichler, M. & van der Aalst, W.M.P. (2017). Trans-national joint research projects : defying the odds of National Inter-University Competition. Business & Information Systems Engineering : The International Journal of WIRTSCHAFTSINFORMATIK, 59(4), 205-206.

van der Aalst, W.M.P., Bichler, M. & Heinzl, A. (2017). Responsible Data Science. Business & Information Systems Engineering : The International Journal of WIRTSCHAFTSINFORMATIK, 59(5), 311-313.

La Rosa, M., Van Der Aalst, W.M.P., Dumas, M. & Milani, F.P. (2017). Business process variability modeling : a survey. ACM Computing Surveys, 50(1):2

De Murillas, E.G.L., Reijers, H.A. & van der Aalst, W.M.P. (2017). Everything you always wanted to know about your process, but did not know how to ask. Business Process Management Workshops (pp. 296-309). (Lecture Notes in Business Information Processing, No. 281). BHRA / Springer Verlag.

Hiemstra, D., Tax, N. & Bockting, S. (2017). Ranking learning-to-rank methods. In N. Ferro, C. Lucchese, M. Maistro & R. Perego (Eds.), Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers (pp. 3-3). (CEUR Workshop Proceedings, No. 2007). Aachen.

Tax, N., Sidorova, N., Haakma, R. & van der Aalst, W.M.P. (2017). Mining process model descriptions of daily life through event abstraction. Intelligent Systems and Applications (Studies in Computational Intelligence, No. 751).

Verbeek, H.M.W. (2017). Decomposed replay using hiding and reduction as abstraction. In J. Kleijn, W. Penchek & M. Koutny (Eds.), Transactions on Petri Nets and Other Models of Concurrency XII (pp. 166-186). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10470 LNCS). Dordrecht: Springer Netherlands.

Assy, N., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Discovering hierarchical consolidated models from process families. Advanced Information Systems Engineering (pp. 314-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10253 LNCS). Dordrecht: Springer.

Bolt Iriondo, A.J., de Leoni, M., van der Aalst, W.M.P. & Gorissen, P. (2017). Business process reporting using process mining, analytic workflows and process cubes : A case study in education. In R.-M. Stefanie & C. Paolo (Eds.), Data-Driven Process Discovery and Analysis (pp. 28-53). (Lecture Notes in Business Information Processing, No. 244). Dordrecht: Springer.

Bolt Iriondo, A.J., van der Aalst, W.M.P. & de Leoni, M. (2017). Finding process variants in event logs (short paper). In W. Gaaloul, H. Panetto, A. Paschke, C. Agostino Ardagna, R. Meersman, M. Papazoglou & C. Debruyne (Eds.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences (pp. 45-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10573 LNCS). Dordrecht: Springer Netherlands.

Chatain, T., Carmona, J. & van Dongen, B.F. (2017). Alignment-based trace clustering. In H. Ma, G. Guizzardi, O. Pastor & H.C. Mayr (Eds.), Conceptual Modeling – 36th International Conference, ER 2017, Proceedings (pp. 295-308). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10650 LNCS). BHRA / Springer Verlag.

Cheng, L. & Li, T. (2017). Efficient data redistribution to speedup big data analytics in large systems. Proceedings of the 23rd IEEE International Conference on High Performance Computing, 19-22 December 2016, Hyderabad, India (pp. 91-100). Piscataway: IEEE.

Cheng, L., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Efficient event correlation over distributed systems. Proceedings – 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017 (pp. 1-10). Piscataway: Institute of Electrical and Electronics Engineers (IEEE).

Cheng, L., Wang, Y., Pei, Y. & Epema, D.H.J. (2017). A coflow-based co-optimization framework for high-performance data analytics. 2017 46th International Conference on Parallel Processing (ICPP), 14-17 August 2017, Bristol, United Kingdom (pp. 392-401). Piscataway: Institute of Electrical and Electronics Engineers (IEEE).

Dalmas, B., Tax, N. & Norre, S. (2017). Heuristics for high-utility local process model mining. In J. Carmona, W. van der Aalst & R. Bergenthum (Eds.), Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data (pp. 106-121). (CEUR Workshops proceedings, No. 1847). CEUR.

Dees, M., de Leoni, M. & Mannhardt, F. (2017). Enhancing process models to improve business performance : a methodology and case studies. In M. Papazoglou, C. Agostino Ardagna, W. Gaaloul, R. Meersman, A. Paschke, C. Debruyne & H. Panetto (Eds.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences (pp. 232-251). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10573 LNCS). Dordrecht: Springer Netherlands.

Dixit, P.M., Garcia Caballero, H.S., Corvo, A., Hompes, B.F.A., Buijs, J.C.A.M. & van der Aalst, W.M.P. (2017). Enabling interactive process analysis with process mining and visual analytics. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (pp. 573-584).

Dixit, P.M., Buijs, J.C.A.M., van der Aalst, W.M.P., Hompes, B.F.A. & Buurman, J. (2017). Using domain knowledge to enhance process mining results. In S. Rinderle-Ma & P. Ceravolo (Eds.), Data-Driven Process Discovery and Analysis (pp. 76-104). (Lecture Notes in Business Information Processing, No. 244). Dordrecht: Springer.

de Leoni, M. & Marrella, A. (2017). How planning techniques can help process mining : the conformance-checking case. Proceedings of the 25th Italian Symposium on Advanced Database (SEBD 2017), 25-29 June 2017, Squillace Lido, Italy

Fani Sani, M., van der Aalst, W.M.P., Bolt Irondo, A.J. & García-Algarra, J. (2017). Subgroup discovery in process mining. In W. Abramowicz (Ed.), Business Information Systems (pp. 237-252). (Lecture Notes in Business Information Processing, No. 288). Dordrecht: Springer.

González López De Murillas, E., Helm, E., Reijers, H.A. & Küng, J. (2017). Audit trails in OpenSLEX : paving the road for process mining in healthcare. In M. Bursa, M. Elena Renda, A. Holzinger & S. Khuri (Eds.), Information Technology in Bio- and Medical Informatics (pp. 82-91). (ITBAM 2017, 8th International Conference on Information Technology in Bio- and Medical Informatics, Lyon, France). Dordrecht: Springer.

Hassani, M., Töws, D., Cuzzocrea, A. & Seidl, T. (2017). BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. Proceedings of BigMine workshop @KDD

Hassani, M., Töws, D. & Seidl, T. (2017). Understanding the bigger picture : Batch-free exploration of streaming sequential patterns with accurate prediction. 32nd Annual ACM Symposium on Applied Computing, SAC 2017 (pp. 866-869). Marrakech, Morocco: ACM

Hompes, B.F.A., Maaradji, Abderrahmane, La Rosa, M., Dumas, M., Buijs, J.C.A.M. & van der Aalst, W.M.P. (2017). Discovering causal factors explaining business process performance. CAiSE 2017 (pp. 177-191). Essen: Springer International Publishing AG.

Hompes, B.F.A., Buijs, J.C.A.M., van der Aalst, W.M.P., Dixit, P.M. & Buurman, J. (2017). Detecting changes in process behavior using comparative case clustering. In S. Rinderle-Ma & P. Ceravolo (Eds.), Data-Driven Process Discovery and Analysis (pp. 54-75). (Lecture Notes in Business Information Processing, No. 244). Dordrecht: Springer.

Jabeen, F., Leopold, H. & Reijers, H.A. (2017). How to make process model matching work better? An analysis of current similarity measures. Business Information Systems – 20th International Conference, BIS 2017, Proceedings (pp. 181-193). (Lecture Notes in Business Information Processing, No. 288). BHRA / Springer Verlag.

Labba, C., Assy, N., Saoud, N.B.B. & Gaaloul, W. (2017). Adaptive deployment of service-based processes into cloud federations. In Q. Li, Y. Gao, W. Jia, L. Chen, F. Dzerzhinskiy, , A. Klimenko, S.V. Klimenko, A. Bouguettaya & X. Zhang (Eds.), Web Information Systems Engineering – WISE 2017 (pp. 275-289). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10569 LNCS). Dordrecht: Springer Netherlands.

Leemans, M. & van der Aalst, W.M.P. (2017). Modeling and discovering cancelation behavior. In W. Galoul, H. Panetto, C. Agostino Ardagna, C. Debruyne, M. Papazoglou & A. Paschke (Eds.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences (pp. 93-113). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10573 LNCS). Dordrecht: Springer Netherlands.

Li, G., de Carvalho, R.M. & Van der Aalst, W.M.P. (2017). Automatic discovery of object-centric behavioral constraint models. In W. Abramowicz (Ed.), Business Information Systems: 20th International Conference, BIS 2017, Poznan, Poland, June 28–30, 2017, Proceedings (pp. 43-58). (Lecture Notes in Business Information Processing, No. 288). Dordrecht: Springer.

Lu, X., Fahland, D., Andrews, R., Suriadi, S., Wynn, M.T., ter Hofstede, A.H.M. & van der Aalst, W.M.P. (2017). Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In C.A. Ardagna, W. Gaaloul, H. Panetto, M. Papazoglou, C. Debruyne, R. Meersman & A. Paschke (Eds.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences (pp. 154-174). (LNCS, No. 10573). Dordrecht: Springer Netherlands.

Lu, Y., Hassani, M. & Seidl, T. (2017). Incremental temporal pattern mining using efficient batch-free stream clustering. SSDBM 2017 (pp. 1-12). New York: ACM.

Mannhardt, F. & Tax, N. (2017). Unsupervised event abstraction using pattern abstraction and local process models. RADAR+EMISA 2017, June 12-13, 2017, Essen, Germany (pp. 55-63). (CEUR Workshop Proceedings, No. 1859). s.l.: CEUR-WS.org.

Mannhardt, F., de Leoni, M., Reijers, H.A. & van der Aalst, W.M.P. (2017). Data-driven process discovery : revealing conditional infrequent behavior from event logs. In Eric Dubois & Klaus Pohl (Eds.), Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings (pp. 545-560). (Lecture Notes in Computer Science, No. 10253). Cham: Springer.

Mannhardt, F. & Blinde, D. (2017). Analyzing the trajectories of patients with sepsis using process mining. RADAR+EMISA 2017, Essen, Germany, June 12-13, 2017 (pp. 72-80). (CEUR Workshop Proceedings, No. 1859). CEUR-WS.org.

Mannhardt, F., de Leoni, M., & Reijers, H.A. (2017). Heuristic mining revamped : an interactive, data-aware, and conformance-aware miner. Proceedings of the Demo Session of the 15th International Conference on Business Process Management (BPM 2017, Barcelona, Spain, September 10-September 15, 2017) (pp. 1-5).

Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P. & Toussaint, P.J. (2017). From low-level events to activities : a pattern-based approach. 8th International Workshop on Enterprise Modelling and Information Systems Architectures (EMISA 2017), 12-13 June 2017, Essen, Germany (pp. 47-47). (Mitteilungen der GI-Fachgruppe Entwicklungsmethoden für Informationssysteme und deren Anwendung, No. 37).

Scharwächter, E., Müller, E., Donges, J., Hassani, M. & Seidl, T. (2017). Detecting change processes in dynamic networks by frequent graph evolution rule mining. 16th IEEE International Conference on Data Mining, ICDM 2016; Barcelona, Catalonia; Spain; 12 December 2016 through 15 December 2016 (pp. 1191-1196). Piscataway: IEEE.

Syamsiyah, A., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Discovering social networks instantly : Moving process mining computations to the database and data entry time. In P. Bera, J. Gulden, I. Reinhartz-Berger, W. Guédria & S. Nurcan (Eds.), Enterprise, Business-Process and Information Systems Modeling (pp. 51-67). (Lecture Notes in Business Information Processing, No. 287). Dordrecht: Springer.

Syamsiyah, A., Bolt Irondo, A.J., Cheng, L., Hompes, B.F.A., Jagadeesh Chandra Bose, R.P., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Business process comparison : a methodology and case study. In W. Abramowicz (Ed.), Business Information Systems (pp. 253-267). (Lecture Notes in Business Information Processing, No. 288). Dordrecht: Springer.

Tax, N., Verenich, I., La Rosa, M. & Dumas, M. (2017). Predictive business process monitoring with LSTM neural networks. In Eric Dubois & Klaus Pohl (Eds.), International Conference on Advanced Information Systems Engineering Springer.

Tax, N., Sidorova, N., van der Aalst, W.M.P. & Haakma, R. (2017). Heuristic approaches for generating local process models through log projections. Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, December 6-9, 2016, Athens, Greece (pp. 1-8). Piscataway: IEEE.

Tax, N., Sidorova, N., Haakma, R. & van der Aalst, W.M.P. (2017). Event abstraction for process mining using supervised learning techniques. In Y. Bi, S. Kapoor & R. Bhatia (Eds.), Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom (pp. 251-269). (LNNS, No. 15). Dordrecht: Springer Netherlands.

Tax, N., Verenich, I., La Rosa, M. & Dumas, M. (2017). Predictive business process monitoring with LSTMs. In E. Postma, G. Fletcher, V. Menkovski, P. van der Putten, W. Duivesteijn, M. Pechenizkiy & J. Vanschoren (Eds.), Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN) (pp. 170-172).

Tax, N., Sidorova, N. & van der Aalst, W.M.P. (2017). Local process models: pattern mining with process models. In E. Postma, G. Fletcher, V. Menkovski, P. van der Putten, W. Duivesteijn, M. Pechenizkiy & J. Vanschoren (Eds.), Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN) (pp. 83-86).

van der Aa, H., Leopold, H. & Reijers, H.A. (2017). Checking process compliance on the basis of uncertain event-to-activity mappings. Advanced Information Systems Engineering – 29th International Conference, CAiSE 2017 (pp. 79-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10253 LNCS). BHRA / Springer Verlag.

Van Der Aalst, W.M.P. (2017). Process cubes : slicing, dicing, rolling up and drilling down event data for process mining. In J. Liu, M.T. Wynn & M. Song (Eds.), Asia Pacific Business Process Management (pp. 1-22). (Lecture Notes in Business Information Processing, No. 159). Dordrecht: Springer Netherlands.

van der Aalst, Wil & Best, Eike (2017). Application and Theory of Petri Nets and Concurrency : 38th International Conference, PETRI NETS 2017 Zaragoza, Spain, June 25–30, 2017 Proceedings. In W.M.P. van der Aalst & E. Best (Eds.), Application and Theory of Petri Nets and Concurrency – 38th International Conference, PETRI NETS 2017, Proceedings (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10258 LNCS). Dordrecht: Springer.

van der Aalst, W.M.P. (2017). Responsible data science : using event data in a “people friendly” manner. In O. Camp, J. Cordeiro, M.M. Missikoff, L.A. Maciaszek & S. Hammoudi (Eds.), Enterprise Information Systems (pp. 3-28). (Lecture Notes in Business Information Processing, No. 291). Dordrecht: Springer.

Van Dongen, B.F., Carmona, J., Chatain, T. & Taymouri, F. (2017). Aligning modeled and observed behavior : a compromise between computation complexity and quality. In E. Dubois & K. Pohl (Eds.), Advanced Information Systems Engineering (pp. 94-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10253 LNCS). Dordrecht: Springer Netherlands.

van Eck, M.L., Sidorova, N. & van der Aalst, W.M.P. (2017). Composite state machine miner : discovering and exploring multi-perspective processes. In C. Cabanillas & L. Azevedo (Eds.), Proceedings of the BPM Demo Track 2016 (pp. 73-77). (CEUR Workshop Proceedings, No. 1789). CEUR-WS.org.

van Eck, M.L., Sidorova, N. & van der Aalst, W.M.P. (2017). Guided interaction exploration in artifact-centric process models. Proceedings of the 19th IEEE Conference on Business Informatics, CBI 2017 IEEE.

van Zelst, S.J., Bolt Iriondo, A.J., Hassani, M., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Online conformance checking: relating event streams to process models using prefix-alignments. Proceedings of BigMine @KDD

van Zelst, S.J., Bolt Iriondo, A.J. & van Dongen, B.F. (2017). Tuning alignment computation : an experimental evaluation. In J. Carmona, W. van der Aalst & R. Bergenthum (Eds.), Algorithms and Theories for the Analysis of Event Data 2017. Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data (ATAED 2017),Zaragoza, Spain, June 26–27, 2017 (pp. 6-20). (CEUR Workshop Proceedings, No. 1847).

Garcia Caballero, H.S., Corvo, A., Dixit, P.M. & Westenberg, M.A. (2017). Visual analytics for evaluating clinical pathways.

Garcia Caballero, H.S., Westenberg, M.A., Verbeek, H.M.W. & van der Aalst, W.M.P. (2017). Visual analytics for soundness verification of process models.

Li, G., Medeiros de Carvalho, R. & van der Aalst, W.M.P. (2017). Automatic discovery of object-centric behavioral constraint models.

Lu, X. & Fahland, D. (2017). A conceptual framework for understanding event data quality in behavior analysis.

Leemans, M. & Liu, C. (2017). XES Software Telemetry Extension. s.l.: XES Working Group, 0 pp.

Leemans, M. & Liu, C. (2017). XES Software Event Extension. s.l.: XES Working Group, 11 pp.

Leemans, M. & Liu, C. (2017). XES Software Communication Extension. s.l.: XES Working Group, 4 pp.

Leemans, S.J.J. (2017). Robust process mining with guarantees. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst & Dirk Fahland).

Ramezani Taghiabadi, E. (2017). Understanding non-compliance. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst & Dirk Fahland).

Firat, M., Dellaert, N.P. & Nuijten, W.P.M. (2017). Incorporating linear optimization into routing problems.