@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{LeopoldvanderAaOffenbergetal.2019,
  author    = {Leopold, Henrik and van der Aa, Han and Offenberg, Jelmer and Reijers, Hajo A.},
  title     = {Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels},
  series = {Information systems},
  volume    = {83},
  journal   = {Information systems},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0306-4379},
  doi       = {10.1016/j.is.2019.02.005},
  pages     = {30 -- 39},
  year      = {2019},
  abstract  = {Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models' activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.},
  language  = {en}
}