Abstract
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.
Highlights
The daily activities and experiences of organizations are represented in Business Processes (BPs) comprising information about the interaction between systems and partners [1]
This result is consistent with previous research, where this measure showed the best results when the BPs elements use vector space representation
Business process clustering using incremental covering arrays and balanced Bayesian information criterion groups is based on the combination of features as well as the number of elements
Summary
The daily activities and experiences of organizations are represented in Business Processes (BPs) comprising information about the interaction between systems and partners [1]. The possibility of identifying these families enables analysts to make a systematic analysis of the BPs of an organization, thereby promoting reuse In this context, several research approaches have been presented for grouping BPs together according to their similarities. Other information retrieval (IR) techniques have been applied to improve the results of such research [7], including the multimodal approach [8] For their part, clustering techniques aim at forming groups of BPs in accordance with common features such as structure, control flow, and tasks [9]. This paper presents ICAClusterBP, an algorithm for improving the visualization of results in a BP search system This algorithm takes as input a set of BPs retrieved from a multimodal search component [6,12] and using Incremental Covering Arrays (ICAs) selects the best clustering solution [13].
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