Process mining for knowledge-intensive business processes

Abstract

In recent years, investigating opportunities to support knowledge-intensive business processes has gained increasing momentum in the research community. Novel contributions that introduce paradigms addressing the need for process execution flexibility form an alternative to traditional workflow management approaches and are mostly subsumed under the concept of adaptive case management (ACM). However, many of these approaches omit mining any kind of knowledge about such processes. This is because there is a gap between process mining, which works well for structured processes, and ACM, which mainly focuses on information system support for task management and collaboration using heterogeneous data sources. In this paper, we strive to bridge this gap by introducing a method for mining knowledge-intensive processes. It is part of agenda-driven case management, an ACM approach that follows the idea of mining common execution patterns while a case manager handles a flexible agenda.