Modified Time-Based Heuristics Miner for Parallel Business Processes

Abstract

Process Mining, or Process Discovery, is a method for modeling the workflow of a business process from event logs. Business process models contain sequential and parallel traces. In this paper, a modification of the frequently used process-mining algorithm Heuristics Miner is proposed. The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log. It can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns; the latter cannot be discovered by the original Heuristics Miner. The threshold intervals are determined on the basis of the average dependency measure in the dependency graph. The experimental results show that the proposed algorithm is able to discover concurrent business processes formed by parallel (AND) and conditional (OR) patterns, whereas the existing Heuristics Miner algorithm can only discover concurrent business processes formed by parallel (AND) patterns. This paper also provides an evaluation of validity an fitness of the discovered process model.