Mining Non-Redundant Local Process Models From Sequence Databases

TL;DR

This paper introduces heuristics for mining non-redundant Local Process Models (LPMs) from sequence databases to reduce redundancy and improve coverage, addressing the pattern explosion problem in process mining.

Contribution

It proposes novel heuristics to extract non-redundant LPMs from redundant models or sequential patterns, enhancing the efficiency of process mining techniques.

Findings

Heuristics effectively reduce redundancy in LPM sets.

Proposed methods improve coverage of observed behaviors.

Compared techniques show better performance in complexity and relevance.

Abstract

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overload the user with too many patterns. Local Process Model (LPM) mining is an alternative approach coming from the field of process mining. While in traditional sequential pattern mining, a pattern describes one subsequence, an LPM captures a set of subsequences. Also, while traditional sequential patterns only match subsequences that are observed in the sequence database, an LPM may capture subsequences that are not explicitly observed, but that are related to observed subsequences. In other words, LPMs generalize the behavior observed in the sequence database. These properties make it possible for a set of LPMs to cover the behavior of a much larger set of sequential patterns. Yet, existing LPM mining techniques still suffer from the pattern explosion problem because they produce sets of redundant LPMs. In this paper, we propose several heuristics to mine a set of non-redundant LPMs either from a set of redundant LPMs or from a set of sequential patterns. We empirically compare the proposed heuristics between them and against existing (local) process mining techniques in terms of coverage, redundancy, and complexity of the produced sets of LPMs.