OrgMiner: A Framework for Discovering User-Related Process Intelligence from Event Logs

Abstract

Process Intelligence refers to the extraction and analysis of valuable knowledge nuggets embedded in business process instances/event logs or enterprise applications, for the purpose of supporting various decision-making processes. Researchers and practitioners mine such event logs using Process Mining and Analytics (PMA) techniques that help analyze business processes across three perspectives: control flow, organization, and data. While previous PMA studies have made advances toward the control flow and data flow perspectives, there is limited research toward the organizational perspective of process intelligence. In this study, we propose an organizational mining framework, OrgMiner, that supports constructing organizational models from event logs. The framework utilizes the notion of behavioral patterns, which rely on the weak order relations appearing in event logs. The various modules and knowledge elements in the framework are described in detail. The components of the framework support identifying, selecting, and applying behavioral patterns using different metrics for organizational mining purposes. The derived organizational models can be used to support decision making in scenarios such as task assignment, resource allocation, as well as role-based access control. Compared to extant studies, the proposed approach does not assume prior availability of explicit process models. Additionally, the process patterns presented in this study can be used as building blocks, so that researchers and practitioners can use them directly or extend them further to identify complex organizational processes. A case study is presented to evaluate the feasibility and effectiveness of the OrgMiner framework.