Towards Measuring Process Model Granularity via Natural Language Analysis

Leopold, Henrik; Pittke, Fabian; Mendling, Jan

doi:10.1007/978-3-319-06257-0_33

Cited by 3 publications

(2 citation statements)

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“…A potential research direction may look into the use of ontologies such as WordNet in order to derive the degree of specialization of a verb. Leopold et al investigate in [72] different strategies that are partially based on WordNet to derive the granularity of a process model. Similarly, Klinkemüller et al [67] use label specificity as indicator for the matching of process model elements.…”

Section: Summary and Discussionmentioning

confidence: 99%

Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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I like things matching. I have an upright bass, a drum kit and a grand piano that's the same color.-Penn Jillette A B S T R A C T Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during process execution. Aiming at a better process understanding and improvement, this event data can be used to analyze processes using process mining techniques. Process models can be automatically discovered and the execution can be checked for conformance to specified behavior. Moreover, existing process models can be enhanced and annotated with valuable information, for example for performance analysis. While the maturity of process mining algorithms is increasing and more tools are entering the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Mapping the recorded events to activities of a given process model is essential for conformance checking, annotation and understanding of process discovery results. Current approaches try to abstract from events in an automated way that does not capture the required domain knowledge to fit business activities. Such techniques can be a good way to quickly reduce complexity in process discovery. Yet, they fail to enable techniques like conformance checking or model annotation, and potentially create misleading process discovery results by not using the known business terminology.In this thesis, we develop approaches that abstract an event log to the same level that is needed by the business. Typically, this abstraction level is defined by a given process model. Thus, the goal of this thesis is to match events from an event log to activities in a given process model. To accomplish this goal, behavioral and linguistic aspects of process models and event logs as well as domain knowledge captured in existing process documentation are taken into account to build semiautomatic matching approaches. The approaches establish a pre-processing for every available process mining technique that produces or annotates a process model, thereby reducing the manual effort for process analysts. While each of the presented approaches can be used in isolation, we also introduce a general framework for the integration of different matching approaches.The approaches have been evaluated in case studies with industry and using a large industry process model collection and simulated event logs. The evaluation demonstrates the effectiveness and efficiency of the approaches and their robustness towards nonconforming execution logs. • Thomas Baier, Andreas Rogge-Solti, Mathias Weske, and Jan Mendling. Matching of events and activities -an approach based on behavioral constraint satisfaction. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 1225-1230. ACM, 2015.• Thomas Baier, Claudio Di Ciccio, Jan Mendling, and Mathias Weske. Matching of events and activities -an approach using declarative modeling constraints. Other important roles i...

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Section: Summary and Discussionmentioning

confidence: 99%

Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

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“…As there is currently no sufficiently effective possibility of measuring the granularity of a process model, the decision about the appropriate level of detail is purely based on the subjective assessment of the modelers. (Leopold, Pittke, & Mendling, 2013) Setting this appropriate level can be thought of as an optimization problem in itself. If a process model is too superficial, it will not contain enough information to draw conclusions, conduct redesign or utilize it in any other ways.…”

Section: Granularity Of Process Modelsmentioning

confidence: 99%