Scalable process discovery and conformance checking.

ABSTRACT: Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framew

SUBMITTER: Leemans SJJ 

PROVIDER: S-EPMC5910523 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature