Business process similarity metric supporting one-to-many relationship

Abstract

In many areas graph match techniques are used to compare and identify common characteristics. In this paper we apply graph similarity techniques on the business processes used inside organizations and extracted with process mining techniques. The scope is to identify if an organization uses a similar process for a specific business case as another organization. However as the existence of exact matching is less probable, error tolerant graph matching techniques are more suitable for real life data. Business processes could have a different granularity level; one business process is more detailed in specific areas than the business process subject of the comparison. The custom algorithm for business process match presented in this paper takes into consideration a one-to-many relation for activities: one activity is matched with a set of activities in the other graph. Such information is important in extracting the common characteristics of organizations and could represent an input for choosing a collaborator. Business processes if not available are extracted with process mining techniques and are reduced to directed graph format. A custom graph similarity algorithm extended for multivalent nodes is applied and a business process similarity factor is retrieved.