Processes meet Big Data: Scaling process discovery algorithms in Big Data environment

Process mining is one of business process management techniques which is used to extract values from process execution logs. Process discovery algorithms, like alpha and heuristic miners, are used to auto-matically discover/rebuild business process models from event logs. However, the performance of these techniques is limited when dealing with Big Data. To cope with this issue, we propose a distributed implementation, based on Spark framework, of the alpha and heuristic algorithms to support efficient scalable process discovery for big process data. The approach consists of distributing the CPU intensive phases, such as the construction of the causality matrix related to these algorithms. Experimental results show that the proposed algorithms speed-up and scale-up well with regard to the variation of both data size and the number of nodes in the cluster. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Process mining is a business process management technique used to extract value from process execution logs. This study proposes a distributed implementation based on the Spark framework for efficient scalable process discovery in big data scenarios. Experimental results demonstrate that the proposed approach achieves significant speed-up and scalability when dealing with large datasets and varying cluster sizes.