CC4Spark: Distributing Event Logs and big complex Conformance Checking problems

Abstract

Conformance checking is one of the disciplines that best exposes the power of process mining, since it allows detecting anomalies and deviations in business processes, helping to assess and improve the quality of these. This is an indispensable task, especially in Big Data environments where large amounts of data are generated, and where the complexity of the processes is increasing. CC4Spark enables com panies to face this challenging scenario in twofold. First, it supports distributing conformance checking alignment problems by means of a Big Data infrastructure based on Apache Spark, allowing users to im port, transform and prepare event logs stored in distributed data sources, and solve them in a distributed environment. Secondly, this tool supports decomposed Petri nets. This helps to noticeably reduce the complexity of the models. Both characteristics help companies in facing increasingly frequent scenar ios with large amounts of logs with highly complex business processes. CC4Spark is not tied to any particular conformance checking algorithm, so that users can employ customised algorithms.