Publications 2018

  • [PDF] [DOI] P. M. Dixit, J. C. A. M. Buijs, H. M. W. Verbeek, and W. M. P. van der Aalst, “Fast incremental conformance analysis for interactive process discovery,” in Business information systems – 21st international conference, bis 2018, proceedings, 2018, pp. 163-175.
    [Bibtex]
    @Conference{Dixit18,
    Title = {Fast Incremental Conformance Analysis for Interactive Process Discovery},
    Author = {Dixit, P. M. and Buijs, J. C. A. M. and Verbeek, H. M. W. and Aalst, W. M. P. van der},
    Booktitle = {Business Information Systems - 21st International Conference, BIS 2018, Proceedings},
    Year = {2018},
    Editor = {Abramowicz, W. and Paschke, A.},
    Pages = {163--175},
    Publisher = {Springer},
    Series = {LNBIP},
    Volume = {320},
    Abstract = {Interactive process discovery allows users to specify domain knowledge while discovering process models with the help of event logs. Typically the coherence of an event log and a process model is calculated using conformance analysis. Many state-of-the-art conformance techniques emphasize on the correctness of the results, and hence can be slow, impractical and undesirable in interactive process discovery setting, especially when the process models are complex. In this paper, we present a framework (and its application) to calculate conformance fast enough to guide the user in interactive process discovery. The proposed framework exploits the underlying techniques used for interactive process discovery in order to incrementally update the conformance results. We trade the accuracy of conformance for performance. However, the user is also provided with some diagnostic information, which can be useful for decision making in an interactive process discovery setting. The results show that our approach can be considerably faster than the traditional approaches and hence better suited in an interactive setting.},
    Doi = {10.1007/978-3-319-93931-5_12},
    Owner = {hverbeek},
    Timestamp = {2018.03.27},
    Url = {http://www.scopus.com/inward/record.url?scp=85050635542&partnerID=8YFLogxK}
    }
  • [PDF] [DOI] P. M. Dixit, H. M. W. Verbeek, and W. M. P. van der Aalst, “Fast conformance analysis based on activity log abstraction,” in 2018 ieee 22nd international enterprise distributed object computing conference, proceedings, 2018, pp. 135-144.
    [Bibtex]
    @InProceedings{Dixit18b,
    Title = {Fast Conformance Analysis based on Activity Log Abstraction},
    Author = {Dixit, P. M. and Verbeek, H. M. W. and Aalst, W. M. P. van der},
    Booktitle = {2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, Proceedings},
    Year = {2018},
    Editor = {Selmin Nurcan and Pontus Johnson},
    Month = {October},
    Pages = {135--144},
    Publisher = {IEEE},
    Abstract = {Process mining techniques focus on bridging the gap between activity logs and business process management. Process discovery is a sub-field of process mining which uses activity logs in order to discover process models. Some process discovery techniques, such as interactive process discovery and genetic algorithms, rely on the so-called conformance analysis. In such techniques, process models are discovered in an incremental way, and the quality of the process models is quantified by the results of conformance analysis. State-of-the-art conformance analysis techniques are typically optimized and devised for one-time use. However, in process discovery settings which are incremental in nature, it is imperative to have fast conformance analysis. Moreover, the activity logs used for conformance analysis at each stage remain the same. In this paper, we propose an approach that exploits this fact in order to expedite conformance analysis by approximating the conformance results. We use an abstracted version of an activity log, which can be used to compare withthe changing (or new) process models in an incremental processdiscovery setting. Our results show that the proposed technique isable to outperform traditional conformance techniques in terms of performance by approximating conformance scores.},
    Doi = {10.1109/EDOC.2018.00026},
    Owner = {hverbeek},
    Timestamp = {2018.07.02},
    Url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8536157}
    }
  • [PDF] P. M. Dixit, H. M. W. Verbeek, and W. M. P. van der Aalst, “Incremental computation of synthesis rules for free-choice petri nets,” in Formal aspects of component software – 15th international conference, facs 2018, proceedings, 2018, pp. 97-117.
    [Bibtex]
  • [PDF] [DOI] P. M. Dixit, H. M. W. Verbeek, J. C. A. M. Buijs, and W. M. P. v. d. Aalst, “Interactive data-driven process model construction,” in 37th international conference on conceptual modeling, er 2018, proceedings, 2018, pp. 251-265.
    [Bibtex]
    @Conference{Dixit18a,
    Title = {Interactive Data-driven Process Model Construction},
    Author = {Dixit, P. M. and Verbeek, H. M. W and Buijs, J. C. A. M. and Aalst, W. M. P. v. d.},
    Booktitle = {37th International Conference on Conceptual Modeling, ER 2018, Proceedings},
    Year = {2018},
    Editor = {Li, Z., and Trujillo, J. C., and Du, X. and Lee, M. L. and Davis, K. C. and Ling, T. W. and Li, G.},
    Month = {October},
    Pages = {251--265},
    Publisher = {Springer},
    Series = {LNCS},
    Volume = {11157},
    Abstract = {Process discovery algorithms address the problem of learning process models from event logs. Typically, in such settings a user's activity is limited to conguring the parameters of the discovery algorithm, and hence the user expertise/domain knowledge can not be incorporated during traditional process discovery. In a setting where the event logs are noisy, incomplete and/or contain uninteresting activities, the process models discovered by discovery algorithms are often inaccurate and/or incomprehensible. Furthermore, many of these automated techniques can produce unsound models and/or cannot discover duplicate activities, silent activities etc. To overcome such shortcomings, we introduce a new concept to interactively discover a process model, by combining a user's domain knowledge with the information from the event log. The discovered models are always sound and can have duplicate activities, silent activities etc. An objective evaluation and a case study shows that the proposed approach can outperform traditional discovery techniques.},
    Comment = {Accepted for publication},
    Doi = {10.1007/978-3-030-00847-5_19},
    Owner = {hverbeek},
    Timestamp = {2018.06.18}
    }
  • [DOI] H. S. Garcia Caballero, M. A. Westenberg, H. M. W. Verbeek, and W. M. P. van der Aalst, “Visual analytics for soundness verification of process models,” in Bpm 2017 international workshops, barcelona, spain, september 10-11, 2017, revised papers, E. Teniente and M. Weidlich, Eds., Springer, 2018, vol. 308, pp. 744-756.
    [Bibtex]
    @InCollection{GarciaCaballero18,
    Title = {Visual analytics for soundness verification of process models},
    Author = {Garcia Caballero, H. S. and Westenberg, M. A. and Verbeek, H. M. W. and Aalst, W. M. P. van der},
    Booktitle = {BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers},
    Publisher = {Springer},
    Year = {2018},
    Editor = {E. Teniente and M. Weidlich},
    Pages = {744--756},
    Series = {Lecture Notes in Business Information Processing},
    Volume = {308},
    Abstract = {Soundness validation of process models is a complex task for process modelers due to all the factors that must be taken into account. Although there are tools to verify this property, they do not provide users with easy information on where soundness starts breaking and under which conditions. Providing insights such as states in which problems occur, involved activities, or paths leading to those states, is crucial for process modelers to better understand why the model is not sound. In this paper we address the problem of validating the soundness property of a process model by using a novel visual approach and a new tool called PSVis (Petri net Soundness Visualization) supporting this approach. The PSVis tool aims to guide expert users through the process models in order to get insights into the problems that cause the process to be unsound.},
    Doi = {10.1007/978-3-319-74030-0_59},
    Owner = {hverbeek},
    Timestamp = {2019.05.15}
    }
  • [PDF] W. L. J. Lee, J. Munoz-Gama, H. M. W. Verbeek, W. M. P. van der Aalst, and M. SepĂșlveda, “Improving merging conditions for recomposing conformance checking,” in Proceedings of the BPI 2018 workshop, 2018.
    [Bibtex]
  • [PDF] [DOI] W. L. J. Lee, H. M. W. Verbeek, J. Munoz-Gama, W. M. P. van der Aalst, and M. SepĂșlveda, “Recomposing conformance: closing the circle on decomposed alignment-based conformance checking in process mining,” Information sciences, vol. 466, pp. 55-91, 2018.
    [Bibtex]
  • [PDF] [DOI] W. Meulemans, W. M. Sonke, B. Speckmann, H. M. W. Verbeek, and K. A. B. Verbeek, “Optimal algorithms for compact linear layouts,” in 34th european workshop on computational geometry (eurocg2018), proceedings, 2018, p. 10:1–10:6.
    [Bibtex]
    @Conference{Meulemans18,
    Title = {Optimal Algorithms for Compact Linear Layouts},
    Author = {Meulemans, W. and Sonke, W. M. and Speckmann, B. and Verbeek, H. M. W. and Verbeek, K. A. B.},
    Booktitle = {34th European Workshop on Computational Geometry (EuroCG2018), Proceedings},
    Year = {2018},
    Month = {March},
    Pages = {10:1--10:6},
    Doi = {10.1109/PacificVis.2018.00010},
    Owner = {hverbeek},
    Timestamp = {2018.02.07}
    }
  • [PDF] D. M. M. Schunselaar and H. M. W. Verbeek, “Task elimination may actually increase throughput time,” arXiv.org 2018.
    [Bibtex]
    @TechReport{Schunselaar18,
    Title = {Task Elimination may Actually Increase Throughput Time},
    Author = {Schunselaar, D. M. M. and Verbeek, H. M. W},
    Institution = {arXiv.org},
    Year = {2018},
    Note = {arXiv identifier 1812.11793},
    Abstract = {The well-known Task Elimination redesign principle suggests to remove unnecessary tasks from a process to improve on time and cost. Although there seems to be a general consensus that removing work can only improve the throughput time of the process, this paper shows that this is not necessarily the case by providing an example that uses plain M/M/c activities. This paper also shows that the Task Automation and Parallelism redesign principles may also lead to longer throughput times. Finally, apart from these negative results, the paper also show under which assumption these redesign principles indeed can only improve the throughput time.},
    Owner = {hverbeek},
    Timestamp = {2019.01.07},
    Url = {http://arxiv.org/abs/1812.11793}
    }
  • [PDF] [DOI] W. M. Sonke, K. A. B. Verbeek, W. Meulemans, H. M. W. Verbeek, and B. Speckmann, “Optimal algorithms for compact linear layouts,” in 2018 ieee pacific visualization symposium, pacificvis 2018, proceedings, 2018, pp. 1-10.
    [Bibtex]
    @Conference{Sonke18,
    Title = {Optimal Algorithms for Compact Linear Layouts},
    Author = {Sonke, W. M. and Verbeek, K. A. B. and Meulemans, W. and Verbeek, H. M. W. and Speckmann, B.},
    Booktitle = {2018 IEEE Pacific Visualization Symposium, PacificVis 2018, Proceedings},
    Year = {2018},
    Month = {May},
    Pages = {1--10},
    Publisher = {IEEE Computer Society},
    Doi = {10.1109/PacificVis.2018.00010},
    Owner = {hverbeek},
    Timestamp = {2018.01.25}
    }
  • [PDF] H. M. W. Verbeek and R. Medeiros de Carvalho, “Log skeletons: a classification approach to process discovery,” arXiv.org 2018.
    [Bibtex]
    @TechReport{Verbeek18,
    Title = {Log Skeletons: A Classification Approach to Process Discovery},
    Author = {Verbeek, H. M. W. and Medeiros de Carvalho, R.},
    Institution = {arXiv.org},
    Year = {2018},
    Note = {arXiv Identifier 1806.08247},
    Abstract = {To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.},
    HowPublished = {arXiv:1806.08247},
    Organization = {arXiv.org},
    Owner = {hverbeek},
    Timestamp = {2018.06.21},
    Url = {https://arxiv.org/abs/1806.08247}
    }
  • [DOI] S. J. van Zelst, B. F. van Dongen, W. M. P. van der Aalst, and H. M. W. Verbeek, “Discovering workflow nets using integer linear programming,” Computing, vol. 100, iss. 5, pp. 529-556, 2018.
    [Bibtex]
    @Article{Zelst18,
    Title = {Discovering workflow nets using integer linear programming},
    Author = {Zelst, S. J. van and Dongen, B. F. van and Aalst, W. M. P. van der and Verbeek, H. M. W.},
    Journal = {Computing},
    Year = {2018},
    Month = {May},
    Number = {5},
    Pages = {529--556},
    Volume = {100},
    Abstract = {Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging process mining task. State-of-the-art process discovery algorithms only discover local control flow patterns and are unable to discover complex, non-local patterns. Region theory based techniques, i.e. an established class of process discovery techniques, do allow for discovering such patterns. However, applying region theory directly results in complex, overfitting models, which is less desirable. Moreover, region theory does not cope with guarantees provided by state-of-the-art process discovery algorithms, both w.r.t. structural and behavioural properties of the discovered process models. In this paper we present an ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets. Moreover, we devise a filtering algorithm, based on the internal working of the ILP-formulation, that is able to cope with the presence of infrequent, exceptional behaviour. We have extensively evaluated the technique using different event logs with different levels of exceptional behaviour. Our experiments show that the presented approach allows us to leverage the inherent shortcomings of existing region-based approaches. The techniques presented are implemented and readily available in the HybridILPMiner package in the open-source process mining tool-kits ProM (http://promtools.org) and RapidProM (http://rapidprom.org).},
    Doi = {10.1007/s00607-017-0582-5},
    Owner = {hverbeek},
    Timestamp = {2017.11.10},
    Url = {https://doi.org/10.1007/s00607-017-0582-5}
    }

 

2 Comments

Leave a Reply