Abstract
AbstractProcess discovery is probably the most interesting, but also most challenging, process mining task. The goal is to take an event log containing example behaviors and create a process model that adequately describes the underlying process. This chapter introduces the baseline approach used in most commercial process mining tools. A simplified event log is used to create a so-calledDirectly-Follows Graph(DFG). This baseline is used to explain the challenges one faces when trying to discover a process model. After introducing DFG discovery, we focus on techniques that are able to discover models allowing for concurrency (e.g., Petri nets, process trees, and BPMN models). The chapter distinguishes two types of approaches able to discover such models: (1)bottom-up process discoveryand (2)top-down process discovery. TheAlpha algorithmis presented as an example of a bottom-up technique. The approach has many limitations, but nicely introduces the idea of discovering local constraints. The basicinductive miningalgorithm is presented as an example of a top-down technique. This approach, combined with frequency-based filtering, works well on most event logs. These example algorithms are used to illustrate the foundations of process discovery.
Publisher
Springer International Publishing
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献