Self-learning Predictor Aggregation for the Evolution of People-Driven Ad-Hoc Processes

  • Christoph Dorn
  • César A. Marín
  • Nikolay Mehandjiev
  • Schahram Dustdar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6896)


Contemporary organisational processes evolve with people’s skills and changing business environments. For instance, process documents vary with respect to their structure and occurrence in the process. Supporting users in such settings requires sophisticated learning mechanisms using a range of inputs overlooked by current dynamic process systems. We argue that analysing a document’s semantics is of uttermost importance to identify the most appropriate activity which should be carried out next. For a system to reliably recommend the next steps suitable for its user, it should consider both the process structure and the involved documents’ semantics. Here we propose a self-learning mechanism which dynamically aggregates a process-based document prediction with a semantic analysis of documents. We present a set of experiments testing the prediction accuracy of the approaches individually then compare them with the aggregated mechanism showing a better accuracy.


document analysis process recommendation people-driven ad-hoc processes document and process evolution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Dorn
    • 1
    • 3
  • César A. Marín
    • 2
  • Nikolay Mehandjiev
    • 2
  • Schahram Dustdar
    • 3
  1. 1.Institute for Software ResearchUniversity of CaliforniaIrvineUSA
  2. 2.Centre for Service ResearchUniversity of ManchesterManchesterUK
  3. 3.Distributed Systems GroupVienna University of TechnologyViennaAustria

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