Presented @BPM2010 conference by Thomas Burkhart (DFKI) and Christoph Dorn (Vienna University of Technology) on Supporting experts and generalists in people-driven process by continuously evaluating what recommendation these people need while updating the underlying ad-hoc process model

1. Self-adjusting Recommendations for People-driven Ad-hoc Processes Sep.14, 2010 Presenters: Thomas Burkhart, Christoph Dorn Christoph Dorn, TUV Thomas Burkhart, DFKI Dirk Werth, DFKI SchahramDustdar, TUV

2. 2 Agenda Motivation State-of-the-art: FlexibilityandProcessRecommendation Conceptionof a Recommendation Model Self-adjustingmechanisms Simulation and Prototype Conclusion

4. While still supporting personalized workflow experiences

5. Evolution over time

7. Sadiq et al.: The ability to handle merely partially defined processes

8. Soffer: Variations of a given model termed short-term flexibility, evolution of processes termed long-term flexibility

9. Greiner and Rahm: Following structured models with the capability of handling unforeseen events

15. Only exist for previously executed tasks by this user

17. Incorporates the best practices

20. Recommendationsuccess =

21. Comparesuccessofuserandcrowd-basedrecommendations

22. Change αbydifference in success

23. The more the user adheres to either user or crowd-based recommendations, the stronger the classification as Eagle or Flock10

25. Theirrespectiveclassifierαispushedtowardsthemiddle

27. Move toclearclassificationwhileavoiding lock-in onceclassifiedRealized via the inclusionof a sigmoid function

28. RecommendationSelf-tuning Flow Process DB 12 Recommendation Provisioning ProcessandClassification Self-Adjustment Personal Process User-CentricRecommendations ProcessRefinement Group Process Group-CentricRecommendations alpha User Profile DB ClassificationAdjustment alpha MergeRecommendations Recommendation DB Store andProvideRecommendations Action-to-RecommendationComparison User Monitor

31. Conclusion 15 Increased effectiveness through combining personal and crowd-based behavior Classification of user types into eagle and flock to utilize personal know-how while considering best practices Continuous user monitoring and classification adjustment Classification self-tuning cycle to find the most efficient value for α

32. 16 Thankyouforyourattention. Ifyouhaveanyquestions, pleasefeelfreetoask. burkhart@iwi.dfki.de dorn@infosys.tuwien.ac.at www.commius.eu