2019

Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts

Posted onAuthorEric VerbeekLeave a comment

Spenrath, Y., & Hassani, M. (2019). Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019 (CEUR Workshop Proceedings; Vol. 2322). CEUR-WS.org. Abstract Bottleneck prediction is an important sub-task of process mining that aims at optimizing Read More …

Efficiently computing alignments: algorithm and datastructures

Posted onAuthorEric VerbeekLeave a comment

van Dongen, B. F. (2019). Efficiently computing alignments: algorithm and datastructures. In F. Daniel, Q. Z. Sheng, & H. Motahari (Eds.), Business Process Management Workshops – BPM 2018 International Workshops, Revised Papers (pp. 44-55). (Lecture Notes in Business Information Processing; Vol. 342). Springer. DOI: 10.1007/978-3-030-11641-5_4 Abstract Conformance checking is considered to be anything where observed Read More …

Detecting behavioral design patterns from software execution data

Posted onAuthorEric VerbeekLeave a comment

Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2019). Detecting behavioral design patterns from software execution data. In E. Damiani, G. Spanoudakis, & L. A. Maciaszek (Eds.), Evaluation of Novel Approaches to Software Engineering – 13th International Conference, ENASE 2018, Revised Selected Papers (pp. 137-164). (Communications in Computer Read More …

Describing behavior of processes with many-to-many interactions

Posted onAuthorEric VerbeekLeave a comment

Fahland, D. (2019). Describing behavior of processes with many-to-many interactions. In S. Haar, & S. Donatelli (Eds.), Application and Theory of Petri Nets and Concurrency – 40th International Conference, PETRI NETS 2019, Proceedings (pp. 3-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11522 LNCS). Read More …

Data-driven usability test scenario creation

Posted onAuthorEric VerbeekLeave a comment

van Eck, M. L., Markslag, E., Sidorova, N., Brosens-Kessels, A., & van der Aalst, W. M. P. (2019). Data-driven usability test scenario creation. In M. K. Lárusdóttir, M. Winckler, K. Kuusinen, P. Palanque, & C. Bogdan (Eds.), Human-Centered Software Engineering – 7th IFIP WG 13.2 International Working Conference, HCSE 2018, Revised Selected Papers (pp. 88-108). Read More …

Concept drift detection of event streams using an adaptive window

Posted onAuthorEric VerbeekLeave a comment

Hassani, M. (2019). Concept drift detection of event streams using an adaptive window. In 33rd International ECMS Conference on Modelling and Simulation, ECMS 2019 (pp. 230-239). [DSM 73] (Proceedings – European Council for Modelling and Simulation, ECMS; Vol. 33). Abstract Process mining is an emerging data mining task of gathering valuable knowledge out of the Read More …

Business process improvement activities: differences in organizational size, culture, and resources

Posted onAuthorEric VerbeekLeave a comment

Beerepoot, I., van de Weerd, I., & Reijers, H. A. (2019). Business process improvement activities: differences in organizational size, culture, and resources. In T. Hildebrandt, B. F. van Dongen, M. Röglinger, & J. Mendling (Eds.), Business Process Management – 17th International Conference, BPM 2019, Proceedings (pp. 402-418). (Lecture Notes in Computer Science (including subseries Lecture Read More …

An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models

Posted onAuthorEric VerbeekLeave a comment

Cuzzocrea, A., Mumolo, E., & Hassani, M. (2019). An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. In Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2953-2959). [8616498] Institute of Electrical and Electronics Engineers. DOI: 10.1109/SMC.2018.00502 Abstract This paper Read More …

An approach for workflow improvement based on outcome and time remaining prediction

Posted onAuthorEric VerbeekLeave a comment

Galdo Seara, L., & De Carvalho, R. M. (2019). An approach for workflow improvement based on outcome and time remaining prediction. In S. Hammoudi, B. Selic, & L. F. Pires (Eds.), MODELSWARD 2019 – Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development (pp. 475-482). Setúbal: SCITEPRESS-Science and Technology Publications, Lda.. DOI: Read More …

A model-based framework to automatically generate semi-real data for evaluating data analysis techniques

Posted onAuthorEric VerbeekLeave a comment

Li, G., de Carvalho, R. M., & van der Aalst, W. M. P. (2019). A model-based framework to automatically generate semi-real data for evaluating data analysis techniques. In J. Filipe, A. Brodsky, M. Smialek, & S. Hammoudi (Eds.), ICEIS 2019 – Proceedings of the 21st International Conference on Enterprise Information Systems (pp. 213-220). SCITEPRESS-Science and Read More …

A general framework to identify software components from execution data

Posted onAuthorEric VerbeekLeave a comment

Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2019). A general framework to identify software components from execution data. In G. Spanoudakis, E. Damiani, L. Maciaszek, & L. Maciaszek (Eds.), ENASE 2019 – Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (pp. Read More …

A framework to evaluate and compare decision-mining techniques

Posted onAuthorEric VerbeekLeave a comment

Jouck, T., de Leoni, M., & Depaire, B. (2019). A framework to evaluate and compare decision-mining techniques. In F. Daniel, Q. Z. Sheng, & H. Motahari (Eds.), Business Process Management Workshops – BPM 2018 International Workshops, Revised Papers (pp. 482-493). (Lecture Notes in Business Information Processing; Vol. 342). Cham: Springer. DOI: 10.1007/978-3-030-11641-5_38 Abstract During the Read More …

Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels

Posted onAuthorEric VerbeekLeave a comment

Leopold, H., van der Aa, H., Offenberg, J., & Reijers, H. A. (2019). Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels. Information Systems, 83, 30-39. DOI: 10.1016/j.is.2019.02.005 Abstract Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Read More …

Process mining in social media: applying object-centric behavioral constraint models

Posted onAuthorEric VerbeekLeave a comment

Li, G., & de Carvalho, R. M. (2019). Process mining in social media: applying object-centric behavioral constraint models. IEEE Access, 7, 84360-84373. [8746275]. DOI: 10.1109/ACCESS.2019.2925105 Abstract The pervasive use of social media (e.g., Facebook, Stack Exchange, and Wikipedia) is providing unprecedented amounts of social data. Data mining techniques have been widely used to extract knowledge Read More …

On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification

Posted onAuthorEric VerbeekLeave a comment

Hassani, M., van Zelst, S. J., & van der Aalst, W. M. P. (2019). On the application of sequential pattern mining primitives to process discovery: overview, outlook and opportunity identification. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), [e1315]. DOI: 10.1002/widm.1315 Abstract Sequential pattern mining (SPM) is a well-studied theme in data mining, in Read More …

Online conformance checking: relating event streams to process models using prefix-alignments

Posted onAuthorEric VerbeekLeave a comment

van Zelst, S. J., Bolt Irondio, A. J., Hassani, M., van Dongen, B. F., & van der Aalst, W. M. P. (2019). Online conformance checking: relating event streams to process models using prefix-alignments. International Journal of Data Science and Analytics, 8(3), 269-284. DOI: 10.1007/s41060-017-0078-6 Abstract Companies often specify the intended behaviour of their business processes Read More …

Information-preserving abstractions of event data in process mining

Posted onAuthorEric VerbeekLeave a comment

Leemans, S. J. J., & Fahland, D. (2019). Information-preserving abstractions of event data in process mining. Knowledge and Information Systems. DOI: 10.1007/s10115-019-01376-9 Abstract Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby Read More …

Generating time-based label refinements to discover more precise process models

Posted onAuthorEric VerbeekLeave a comment

Tax, N., Alasgarov, E. E., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2019). Generating time-based label refinements to discover more precise process models. Journal of Ambient Intelligence and Smart Environments, 11(2), 165-182. DOI: 10.3233/AIS-190519 Abstract Process mining is a research field focused on the analysis of event data with the aim Read More …

BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams

Posted onAuthorEric VerbeekLeave a comment

Hassani, M., Töws, D., Cuzzocrea, A., & Seidl, T. (2019). BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. International Journal of Data Science and Analytics, 8(3), 223-239. DOI: 10.1007/s41060-017-0084-8 Abstract Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining Read More …

Real-Time Process Mining for Customer Journey Data

Posted onAuthorEric VerbeekLeave a comment

Available process discovery have been tested in the customer journey context under offline settings. Recent online process discovery approaches like: https://ieeexplore.ieee.org/document/7376771 bring however a lot of added value for a real-time customer journey optimization. The objective of this assignment is to use two different customer journey datasets to test the effectiveness of such approaches for Read More …

Finding Patterns in Evolving Graphs

Posted onAuthorEric VerbeekLeave a comment

The analysis of the temporal evolution of dynamic graphs like social networks is a key challenge for understanding complex processes hidden in graph structured data. Graph evolution rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing rule discovery methods make restrictive assumptions on the Read More …

Using Sequential Pattern Mining to Detect Drifts in Streaming Data

Posted onAuthorEric VerbeekLeave a comment

BFSPMiner is an effective and efficient batch-free algorithm for mining sequential patterns over data streams was published very recently https://link.springer.com/article/10.1007/s41060-017-0084-8. An implementation of the algorithm is available here: https://github.com/Xsea/BFSPMiner. As BFSPMiner has proven to be effective (see Figures 10-14 of the paper) in different domains (see Table 1 in the paper), we would like to Read More …

Discovering high-level BPMN process models from event data

Posted onAuthorEric VerbeekLeave a comment

Kalenkova, Anna, Burattin, Andrea, de Leoni, Massimiliano, van der Aalst, Wil & Sperduti, Alessandro (2019). Discovering high-level BPMN process models from event data. Business Process Management Journal, 25(5), 995-1019. DOI: 10.1108/BPMJ-02-2018-0051 Abstract Purpose: The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using Read More …

Discovering more precise process models from event logs by filtering out chaotic activities

Posted onAuthorEric VerbeekLeave a comment

Tax, N., Sidorova, N. & van der Aalst, W.M.P. (2019). Discovering more precise process models from event logs by filtering out chaotic activities. Journal of Intelligent Information Systems, 52(1), 107-139. DOI: 10.1007/s10844-018-0507-6 Abstract Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that Read More …

2IMI15 Metamodelling and Interoperability

Posted onAuthorEric VerbeekLeave a comment

Independently developed applications based on different models and implemented on different platforms need to use each others services and share each others data. Interoperability is therefore one of the buzz-words of the last years in Computer Science. Web services-driven Service-Oriented Architectures (SOA) have arisen as a solution to the interoperability problem. In this context, metamodeling Read More …

2IMI20 Advanced Process Mining

Posted onAuthorEric VerbeekLeave a comment

Process mining provides a new means to understand and improve processes in an objective way in a variety of application domains through the analysis of recorded event data. This advanced course on process mining teaches students the fundamental concepts and theoretical foundations of process mining along a complete process mining methodology, and exposes students to Read More …

JM0200 Data Entrepreneurship in Action 3

Posted onAuthorEric VerbeekLeave a comment

In this course, students will get the possibility to apply the methods and techniques acquired in parallel courses. The task for the students is to technically analyze the data received by identifying the right techniques to use, implementing them in a repeatable form, and reflecting on the validity of their results and the suitability of Read More …