@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}
}
@article{YousfiWeske2019,
  author    = {Yousfi, Alaaeddine and Weske, Mathias},
  title     = {Discovering commute patterns via process mining},
  series = {Knowledge and Information Systems},
  volume    = {60},
  journal   = {Knowledge and Information Systems},
  number    = {2},
  publisher = {Springer},
  address   = {London},
  issn      = {0219-1377},
  doi       = {10.1007/s10115-018-1255-1},
  pages     = {691 -- 713},
  year      = {2019},
  abstract  = {Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today's challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users' significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far.},
  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}
}