Self-Adjusting Recommendations for People-Driven Ad-Hoc Processes
A company's ability to flexibly adapt to changing business requirements is one key factor to remain competitive. The required flexibility in people driven 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.