Detecting Surprising Situations in Event Data

TL;DR

This paper introduces a novel approach to identify surprising and potentially problematic process situations by framing it as a context-sensitive anomaly detection problem, improving process enhancement insights.

Contribution

It formulates process improvement as detecting surprising situations in event data, capturing anomalies beyond known problematic instances.

Findings

Effective detection of surprising process situations in real-life logs

Outperforms traditional outlier detection methods in process analysis

Provides new insights for process reengineering

Abstract

Process mining is a set of techniques that are used by organizations to understand and improve their operational processes. The first essential step in designing any process reengineering procedure is to find process improvement opportunities. In existing work, it is usually assumed that the set of problematic process instances in which an undesirable outcome occurs is known prior or is easily detectable. So the process enhancement procedure involves finding the root causes and the treatments for the problem in those process instances. For example, the set of problematic instances is considered as those with outlier values or with values smaller/bigger than a given threshold in one of the process features. However, on various occasions, using this approach, many process enhancement opportunities, not captured by these problematic process instances, are missed. To overcome this issue, we formulate finding the process enhancement areas as a context-sensitive anomaly/outlier detection problem. We define a process enhancement area as a set of situations (process instances or prefixes of process instances) where the process performance is surprising. We aim to characterize those situations where process performance/outcome is significantly different from what was expected considering its performance/outcome in similar situations. To evaluate the validity and relevance of the proposed approach, we have implemented and evaluated it on several real-life event logs.