@book{HerzbergWeske2013,
  author    = {Herzberg, Nico and Weske, Mathias},
  title     = {Enriching raw events to enable process intelligence : research challenges},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-241-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-64012},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {30},
  year      = {2013},
  abstract  = {Business processes are performed within a company's daily business. Thereby, valuable data about the process execution is produced. The quantity and quality of this data is very dependent on the process execution environment that reaches from predominantly manual to fullautomated. Process improvement is one essential cornerstone of business process management to ensure companies' competitiveness and relies on information about the process execution. Especially in manual process environments data directly related to the process execution is rather sparse and incomplete. In this paper, we present an approach that supports the usage and enrichment of process execution data with context data - data that exists orthogonally to business process data - and knowledge from the corresponding process models to provide a high-quality event base for process intelligence subsuming, among others, process monitoring, process analysis, and process mining. Further, we discuss open issues and challenges that are subject to our future work.},
  language  = {de}
}
@article{HerzbergMeyerWeske2015,
  author    = {Herzberg, Nico and Meyer, Andreas and Weske, Mathias},
  title     = {Improving business process intelligence by observing object state transitions},
  series = {Data \& knowledge engineering},
  volume    = {98},
  journal   = {Data \& knowledge engineering},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0169-023X},
  doi       = {10.1016/j.datak.2015.07.008},
  pages     = {144 -- 164},
  year      = {2015},
  abstract  = {During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events. (C) 2015 Elsevier B.V. All rights reserved.},
  language  = {en}
}