Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration
Thaís Rodrigues Neubauer, Jari Peeperkorn, Sarajane Marques Peres, Jochen De Weerdt, Marcelo Fantinato · 2023
Process mining encompasses a series of tasks aimed at automatically unveiling knowledge about business processes from event logs registered in underlying information systems deployed in organizations. As well as numerous machine learning approaches, process mining approaches often require a vector space as input. However, the choice of the representational scheme to map event log information to a vector space sig-nificantly influences the quality of the results. This mapping poses challenges due to the diverse information in event logs and the intricate relationships within a business process. Relying solely on automated approaches may overlook relevant information, necessitating the incorporation of domain knowledge from external sources. Unfortunately, this incorporation introduces complexity. To address these inherent issues in constructing adequate vector spaces for process mining, this paper proposes a novel approach leveraging graph embedding to organize process-related information. To this end, we present a novel and highly flexible graph structure to represent process-related information that is then mapped to a dense vector space by applying the metapath2vec algorithm. The resulting dense vector space was compared to traditional vector spaces in an exploratory study, in which we solved the trace clustering task. We employ the N3 measure to assess the quality of the clusters and to verify whether domain knowledge is adequately represented in the vector spaces. The results demonstrate a superior potential of the dense vector spaces obtained via graph embedding to adequately organize the information to be submitted to the trace clustering task.