Conceptual Model with Built-in Process Mining

TL;DR

This paper proposes a unified conceptual model that integrates process mining directly into the system's structure, enabling self-process mining and continuous model enrichment from event logs.

Contribution

It introduces a multilevel conceptual model with built-in process mining capabilities, allowing dynamic, model-driven analysis and evolution based on event logs.

Findings

Framework enables self-process mining within the conceptual model

Case studies demonstrate model evolution from event logs

Achieves comprehensive process analysis beyond initial logs

Abstract

Process mining involves discovering, monitoring, and improving real processes by extracting knowledge from event logs in information systems. Process mining has become an important topic in recent years, as evidenced by a growing number of case studies and commercial tools. Current studies in this area assume that event records are created separately from a conceptual model (CM). Techniques are then used to discover missing processes and conformance with the CM, as well as for checks and enhancements. By contrast, in this paper we focus on modeling events as part of a tight multilevel CM that includes a static description, dynamics, events-log scheme, and monitoring and control system. If there is an out-of-model event log, it is treated as a requirement needed to build or enrich the CM. The motivation for such a unified system is our thesis that process mining is an essential component of a CM with built-in mining capabilities to perform self-process mining and attain completeness. Accordingly, our proposed conceptual model facilitates collecting data generated about itself. The resultant framework emphasizes an integrated representation of systems to include process-mining functionalities. Case studies that start with event logs are recast to evolve around a model-first approach that is not limited to the initial event log. The result presents a framework that achieves the aims of process mining in a more comprehensive way