A business process clustering algorithm using incremental covering arrays to explore search space and balanced Bayesian information criterion to evaluate quality of solutions

The reuse of business processes (BPs) requires similarities between them to be suitably identified. Various approaches have been introduced to address this problem, but many of them feature a high computational cost and a low level of automation. This paper presents a clustering algorithm that groups business processes retrieved from a multimodal search system (based on textual and structural information). The algorithm is based on Incremental Covering Arrays (ICAs) with different alphabets to determine the possible number of groups to be created for each row of the ICA. The proposed algorithm also incorporates Balanced Bayesian Information Criterion to determine the optimal number of groups and the best solution for each query. Experimental evaluation shows that the use of ICAs with strength four (4) and different alphabets reduces the number of solutions needed to be evaluated and optimizes the number of clusters. The proposed algorithm outperforms other algorithms in various measures (precision, recall, and F-measure) by between 12% and 88%. Friedman and Wilcoxon non-parametric tests gave a 90–95% significance level to the obtained results. Better options of repository search for BPs help companies to reuse them. By thus reusing BPs, managers and analysts can more easily get to know the evolution and trajectory of the company processes, a situation that could be expected to lead to improved managerial and commercial decision making.
We acknowledge the FEDER funds under grant TIN2016-75850-R; ABACUSCINVESTAV CONACyT grant EDOMEX-2011-COI-165873 and CGSTICXiuhcoatl-CINVESTAV for providing access to high performance computing. The project that has funded partially the research reported in this paper is: 238469—CONACyT Exact Methods for Building Optimal Covering Arrays (Métodos Exactos para Construir Covering Arrays Óptimos).