@article{WeidlichMendlingWeske2011,
  author    = {Weidlich, Matthias and Mendling, Jan and Weske, Mathias},
  title     = {Efficient consistency measurement based on behavioral profiles of process models},
  series = {IEEE transactions on software engineering},
  volume    = {37},
  journal   = {IEEE transactions on software engineering},
  number    = {3},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {0098-5589},
  doi       = {10.1109/TSE.2010.96},
  pages     = {410 -- 429},
  year      = {2011},
  abstract  = {Engineering of process-driven business applications can be supported by process modeling efforts in order to bridge the gap between business requirements and system specifications. However, diverging purposes of business process modeling initiatives have led to significant problems in aligning related models at different abstract levels and different perspectives. Checking the consistency of such corresponding models is a major challenge for process modeling theory and practice. In this paper, we take the inappropriateness of existing strict notions of behavioral equivalence as a starting point. Our contribution is a concept called behavioral profile that captures the essential behavioral constraints of a process model. We show that these profiles can be computed efficiently, i.e., in cubic time for sound free-choice Petri nets w.r.t. their number of places and transitions. We use behavioral profiles for the definition of a formal notion of consistency which is less sensitive to model projections than common criteria of behavioral equivalence and allows for quantifying deviation in a metric way. The derivation of behavioral profiles and the calculation of a degree of consistency have been implemented to demonstrate the applicability of our approach. We also report the findings from checking consistency between partially overlapping models of the SAP reference model.},
  language  = {en}
}
@article{BaierMendlingWeske2014,
  author    = {Baier, Thomas and Mendling, Jan and Weske, Mathias},
  title     = {Bridging abstraction layers in process mining},
  series = {Information systems},
  volume    = {46},
  journal   = {Information systems},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0306-4379},
  doi       = {10.1016/j.is.2014.04.004},
  pages     = {123 -- 139},
  year      = {2014},
  abstract  = {While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company. (C) 2014 Elsevier Ltd. All rights reserved.},
  language  = {en}
}
@article{BaierDiCiccioMendlingetal.2018,
  author    = {Baier, Thomas and Di Ciccio, Claudio and Mendling, Jan and Weske, Mathias},
  title     = {Matching events and activities by integrating behavioral aspects and label analysis},
  series = {Software and systems modeling},
  volume    = {17},
  journal   = {Software and systems modeling},
  number    = {2},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1619-1366},
  doi       = {10.1007/s10270-017-0603-z},
  pages     = {573 -- 598},
  year      = {2018},
  abstract  = {Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.},
  language  = {en}
}