Event Abstraction for Enterprise Collaboration Systems to Support Social Process Mining

TL;DR

This paper introduces a tailored event abstraction method for enterprise collaboration systems, enabling effective social process mining by converting detailed logs into high-level, interpretable process models.

Contribution

It presents ECSEA, a novel approach that automatically abstracts low-level ECS logs into high-level traces, addressing unique challenges of ECS data for process mining.

Findings

ECSEA produces accurate high-level abstractions from ECS logs.

The approach improves interpretability of collaborative activities.

It enables social process mining in enterprise collaboration systems.

Abstract

One aim of Process Mining (PM) is the discovery of process models from event logs of information systems. PM has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). ECS event logs are very fine-granular and PM applied to their logs results in spaghetti models. A common solution for this is event abstraction, i.e., converting low-level logs into more abstract high-level logs before running discovery algorithms. ECS logs come with special characteristics that have so far not been fully addressed by existing event abstraction approaches. We aim to close this gap with a tailored ECS event abstraction (ECSEA) approach that trains a model by comparing recorded actual user activities (high-level traces) with the system-generated low-level traces (extracted from the ECS). The model allows us to automatically convert future low-level traces into an abstracted high-level log that can be used for PM. Our evaluation shows that the algorithm produces accurate results. ECSEA is a preprocessing method that is essential for the interpretation of collaborative work activity in ECS, which we call Social Process Mining.