Formulating the potentials of clustering of event data over multiple entities for decision support: a network embeddings approach

Abstract

ABSTRACTEvent data from business processes evidence their patterns, behaviors, and dysfunctions. Analytics techniques like clustering and sorting can reveal relevant insights, when data are correlated with a single case identifier. However, when multiple entities are involved, unidimensional models are challenged. We introduce a novel method for analyzing business processes involving multiple interacting entity types. Our approach employs embedding representations to capture pairwise similarities among entity types and their interrelationships. An optimization problem encompasses similarity matrices, cross-entity relationship matrices, and embeddings. An iterative algorithm refines this model, yielding embedding representations and cluster assignments for each entity type. Formulating our method across three diverse business scenarios demonstrates its practicality and potential. Our results, through a proof of concept using real-world data, underscore the value of accounting for the multifaceted nature of business processes, showing substantial improvements and qualitative distinctions compared to unidimensional models.KEYWORDS: Process analyticsmultiple entitiesclusteringnetwork embeddingsdecision supportproblem formulation Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. https://www.win.tue.nl/bpi/2017/challenge.html