@article{KunzeWeidlichWeske2015,
  author    = {Kunze, Matthias and Weidlich, Matthias and Weske, Mathias},
  title     = {Querying process models by behavior inclusion},
  series = {Software and systems modeling},
  volume    = {14},
  journal   = {Software and systems modeling},
  number    = {3},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1619-1366},
  doi       = {10.1007/s10270-013-0389-6},
  pages     = {1105 -- 1125},
  year      = {2015},
  abstract  = {Business processes are vital to managing organizations as they sustain a company's competitiveness. Consequently, these organizations maintain collections of hundreds or thousands of process models for streamlining working procedures and facilitating process implementation. Yet, the management of large process model collections requires effective searching capabilities. Recent research focused on similarity search of process models, but querying process models is still a largely open topic. This article presents an approach to querying process models that takes a process example as input and discovers all models that allow replaying the behavior of the query. To this end, we provide a notion of behavioral inclusion that is based on trace semantics and abstraction. Additional to deciding a match, a closeness score is provided that describes how well the behavior of the query is represented in the model and can be used for ranking. The article introduces the formal foundations of the approach and shows how they are applied to querying large process model collections. An experimental evaluation has been conducted that confirms the suitability of the solution as well as its applicability and scalability in practice.},
  language  = {en}
}
@article{MendlingWebervanderAalstetal.2018,
  author    = {Mendling, Jan and Weber, Ingo and van der Aalst, Wil and Brocke, Jan Vom and Cabanillas, Cristina and Daniel, Florian and Debois, Soren and Di Ciccio, Claudio and Dumas, Marlon and Dustdar, Schahram and Gal, Avigdor and Garcia-Banuelos, Luciano and Governatori, Guido and Hull, Richard and La Rosa, Marcello and Leopold, Henrik and Leymann, Frank and Recker, Jan and Reichert, Manfred and Reijers, Hajo A. and Rinderle-Ma, Stefanie and Solti, Andreas and Rosemann, Michael and Schulte, Stefan and Singh, Munindar P. and Slaats, Tijs and Staples, Mark and Weber, Barbara and Weidlich, Matthias and Weske, Mathias and Xu, Xiwei and Zhu, Liming},
  title     = {Blockchains for Business Process Management},
  series = {ACM Transactions on Management Information Systems},
  volume    = {9},
  journal   = {ACM Transactions on Management Information Systems},
  number    = {1},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {2158-656X},
  doi       = {10.1145/3183367},
  pages     = {1 -- 16},
  year      = {2018},
  abstract  = {Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.},
  language  = {en}
}
@article{vanderAaLeopoldWeidlich2020,
  author    = {van der Aa, Han and Leopold, Henrik and Weidlich, Matthias},
  title     = {Partial order resolution of event logs for process conformance checking},
  series = {Decision support systems : DSS},
  volume    = {136},
  journal   = {Decision support systems : DSS},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  issn      = {0167-9236},
  doi       = {10.1016/j.dss.2020.113347},
  pages     = {12},
  year      = {2020},
  abstract  = {While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.},
  language  = {en}
}