Self-adjusting recommendations for people-driven ad-hoc processes

  • Authors:
  • Christoph Dorn;Thomas Burkhart;Dirk Werth;Schahram Dustdar

  • Affiliations:
  • Vienna University of Technology, Vienna, Austria;Institute for Information Systems, German Research Center for Artificial Intelligence, Saarbrücken, Germany;Institute for Information Systems, German Research Center for Artificial Intelligence, Saarbrücken, Germany;Vienna University of Technology, Vienna, Austria

  • Venue:
  • BPM'10 Proceedings of the 8th international conference on Business process management
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

A company's ability to flexibly adapt to changing business requirements is one key factor to remain competitive. The required flexibility in peopledriven processes is usually achieved through ad-hoc workflows. Effective guidance in ad-hoc workflows requires simultaneous consideration of multiple goals: support of individual work habits, exploration of crowd process knowledge, and automatic adaptation to changes. This paper presents a self-adjusting approach for providing context-sensitive process recommendations based on the analysis of user behavior, crowd processes, and continuous application of process detection. Specifically, we classify users as eagles (i.e., specialists) or flock. The approach is evaluated in the context of the European research project Commius.