Discovering expressive process models by clustering log traces

• Published in: IEEE transactions on knowledge and data engineering Vol. 18; no. 8; pp. 1010 - 1027
• Main Authors: Greco, G., Guzzo, A., Pontieri, L., Sacca, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2006; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; association rules; classification; Clustering; Clustering algorithms; Companies; Computational efficiency; Computer science; control theory; systems; Computer Society; Customer relationship management; Data mining; Data processing. List processing. Character string processing; Enterprise resource planning; Exact sciences and technology; Exact solutions; Information systems. Data bases; Iterative algorithms; Management information systems; Mathematical analysis; Mathematical models; Memory organisation. Data processing; Mining; Process mining; Semantics; Software; Studies; Supply chain management; workflow management; Cluster analysis; Process mining; Data analysis; Scalability; Association rule; association rules; Workflow; Information extraction; Data mining; classification; Greedy algorithm; clustering; workflow management; Workflow management software
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2006.123

Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.