Publications 2018

  • [PDF] 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 BIS 2018 proceedings, 2018.
    [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 = {{BIS 2018} proceedings},
    Year = {2018},
    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.},
    Comment = {Accepted for publication},
    Owner = {hverbeek},
    Timestamp = {2018.03.27}
    }
  • [PDF] P. M. Dixit, H. M. W. Verbeek, and W. M. P. van der Aalst, “Fast conformance analysis based on activity log abstraction,” in EDOC 2018 proceedings, 2018.
    [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 = {{EDOC} 2018 Proceedings},
    Year = {2018},
    Note = {Accepted for publication},
    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.},
    Owner = {hverbeek},
    Timestamp = {2018.07.02}
    }
  • [PDF] P. M. Dixit, H. M. W. Verbeek, and W. M. P. van der Aalst, “Incremental synthesis rules computation for free-choice petri nets,” in FACS 2018 proceedings, 2018.
    [Bibtex]
    @Conference{Dixit18c,
    Title = {Incremental Synthesis Rules Computation for Free-Choice Petri nets},
    Author = {Dixit, P. M. and Verbeek, H. M. W. and Aalst, W. M. P. van der},
    Booktitle = {{FACS 2018} Proceedings},
    Year = {2018},
    Abstract = {In this paper, we propose a novel approach that calculates all the possible applications of synthesis rules, for well-formed free-choice Petri nets, in a speedy way to enable an interactive editing system. The proposed approach uses a so-called incremental synthesis structure, which can be used to extract all the synthesis rules, corresponding to a given net. Furthermore, this structure is updated incrementally, i.e. after usage of a synthesis rule, to obtain the incremental synthesis structure of the newly synthesized net. We prove that the proposed approach is correct and complete in order to synthesize any well-formed free-choice Petri net, starting with an initial well-formed atomic net and the corresponding incremental synthesis structure. A variant of the proposed approach has been implemented that allows interactive modeling (discovery) of sound business processes (from event logs). Experimental results show that the proposed approach is fast, and outperforms the baseline, and hence is well-suited for enabling interactive synthesis of very large nets.},
    Comment = {Accepted for publication},
    Owner = {hverbeek},
    Timestamp = {2018.08.09}
    }
  • [PDF] 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 ER 2018 proceedings, 2018.
    [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 = {{ER 2018} Proceedings},
    Year = {2018},
    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},
    Owner = {hverbeek},
    Timestamp = {2018.06.18}
    }
  • [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]
    @InProceedings{Lee18,
    Title = {Improving merging conditions for recomposing conformance checking},
    Author = {Lee, W. L. J. and Munoz-Gama, J. and Verbeek, H. M. W. and Aalst, W. M. P. van der and Sep{\'u}lveda, M.},
    Booktitle = {Proceedings of the {BPI 2018} workshop},
    Year = {2018},
    Abstract = {Efficient conformance checking is a hot topic in the field of process mining. Much of the recent work focused on improving the scalability of alignment-based approaches to support the larger and more complex processes. This is needed because process mining is increasingly applied in areas where models and logs are “big”. Decomposition techniques are able to achieve significant performance gains by breaking down a conformance problem into smaller ones. Moreover, recent work showed that the alignment problem can be resolved in an iterative manner by alternating between aligning a set of decomposed subcomponents before merging the computed sub-alignments and recomposing subcomponents to fix merging issues. Despite experimental results showing the gain of applying recomposition in large scenarios, there is still a need for improving the merging step, where log traces can take numerous recomposition steps before reaching the required merging condition. This paper contributes by defining and structuring the recomposition step, and proposes strategies with significant performance improvement on synthetic and real-life datasets over both the state-of-the-art decomposed and monolithic approaches.},
    Owner = {hverbeek},
    Timestamp = {2018.06.27}
    }
  • [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]
    @Article{Lee18a,
    Title = {Recomposing conformance: Closing the circle on decomposed alignment-based conformance checking in process mining},
    Author = {Lee, W. L. J. and Verbeek, H. M. W. and Munoz-Gama, J. and Aalst, W. M. P. van der and Sep{\'u}lveda, M.},
    Journal = {Information Sciences},
    Year = {2018},
    Month = {October},
    Pages = {55--91},
    Volume = {466},
    Abstract = {In the area of process mining, efficient conformance checking is one of the main challenges. Several process mining vendors are in the process of implementing conformance checking in their tools to allow the user to check how well a model fits an event log. Current approaches for conformance checking are monolithic and compute exact fitness values but this may take excessive time. Alternatively, one can use a decomposition approach, which runs much faster but does not always compute an exact fitness value.
    This paper introduces a recomposition approach that takes the best of both: it returns the exact fitness value by using the decomposition approach in an iterative manner. Results show that similar speedups can be obtained as by using the decomposition approach, but now the exact fitness value is guaranteed. Even better, this approach supports a configurable time-bound: “Give me the best fitness estimation you can find within 10 minutes.” In such a case, the approach returns an interval that contains the exact fitness value. If such an interval is sufficiently narrow, there is no need to spend unnecessary time to compute the exact value.},
    Doi = {10.1016/j.ins.2018.07.026},
    Keywords = {Conformance checking
    Process mining
    Business process management},
    Owner = {hverbeek},
    Timestamp = {2018.07.16}
    }
  • [PDF] W. Meulemans, W. M. Sonke, B. Speckmann, H. M. W. Verbeek, and K. A. B. Verbeek, “Optimal algorithms for compact linear layouts,” in EuroCG-18 workshop, 2018.
    [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 = {{EuroCG-18} workshop},
    Year = {2018},
    Comment = {Accepted for presentation},
    Owner = {hverbeek},
    Timestamp = {2018.02.07}
    }
  • [PDF] W. M. Sonke, K. A. B. Verbeek, W. Meulemans, H. M. W. Verbeek, and B. Speckmann, “Optimal algorithms for compact linear layouts,” in PacificVis 2018 symposium, 2018.
    [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 = {{PacificVis 2018} symposium},
    Year = {2018},
    Comment = {Accepted for publication},
    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}
    }

 

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