Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data

TL;DR

This paper addresses the challenge of event-case correlation in click data for process mining by proposing a neural network-based method to aggregate user interactions into cases, validated through a user study and expert interviews.

Contribution

It introduces a novel neural network approach for event-case correlation in click data, enabling better process mining analysis of user interaction logs.

Findings

Neural network method effectively aggregates click data into meaningful cases.

Qualitative validation shows improved event log quality for process mining.

Expert interviews confirm the practical relevance of the approach.

Abstract

Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.