@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{WeidlichZiekowGaletal.2014,
  author    = {Weidlich, Matthias and Ziekow, Holger and Gal, Avigdor and Mendling, Jan and Weske, Mathias},
  title     = {Optimizing event pattern matching using business process models},
  series = {IEEE transactions on knowledge and data engineering},
  volume    = {26},
  journal   = {IEEE transactions on knowledge and data engineering},
  number    = {11},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {1041-4347},
  doi       = {10.1109/TKDE.2014.2302306},
  pages     = {2759 -- 2773},
  year      = {2014},
  abstract  = {A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.},
  language  = {en}
}