Information systems log data during the execution of business processes in so called "event logs". Process mining aims to improve business processes by extracting knowledge from event logs. Currently, the de-facto standard for storing and managing event data, XES, is tailored towards sequential access of this data. Handling more and more data in process mining applications is an important challenge and there is a need for standardized ways of storing and processing event data in the large. In this paper, we first discuss several solutions to address the "big data" problem in process mining. We present a new framework for dealing with large event logs using a relational data model which is backwards compatible with XES. This framework, called Relational XES, provides buffered, random access to events resulting in a reduction of memory usage and we present experiments with existing process mining applications to show how this framework trades memory for CPU time.
|Number of pages||8|
|Publication status||Published - 2015|