@article{WeskevanderAalstVerbeek2004,
  author    = {Weske, Mathias and van der Aalst, Wil M. P. and Verbeek, H. M. W.},
  title     = {Advances in business process management},
  issn      = {0169-023X},
  year      = {2004},
  language  = {en}
}
@article{RozinatVanderAalst2006,
  author    = {Rozinat, A and Van der Aalst, Wil M. P.},
  title     = {Conformance testing: Measuring the fit and appropriateness of event logs and process models},
  year      = {2006},
  abstract  = {Most information systems log events (e.g., transaction logs, audit traits) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: "Does the event log conform to the process model and vice versa?". This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework},
  language  = {en}
}
@book{RoggeSoltiMansvanderAalstetal.2013,
  author    = {Rogge-Solti, Andreas and Mans, Ronny S. and van der Aalst, Wil M. P. and Weske, Mathias},
  title     = {Repairing event logs using stochastic process models},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-258-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-66797},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {19},
  year      = {2013},
  abstract  = {Companies strive to improve their business processes in order to remain competitive. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient's treatment. As a result, events or timestamps may be missing or incorrect. In this paper, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks. We evaluate the results using both synthetic data and real event data from a Dutch hospital.},
  language  = {en}
}