A Task Taxonomy for Conformance Checking

2025-07-16

AI Summary

Existing visualization tools for compliance checking lack systematic characterization of analytical tasks, hindering rigorous effectiveness evaluation. This paper introduces the first multidimensional task taxonomy specifically designed for compliance checking, modeling core trace-to-model alignment tasks in process mining along six dimensions: objective, method, constraint type, data characteristics, data target, and cardinality. Crucially, this taxonomy explicitly links the semantic requirements of compliance checking with established visual analytics design principles—thereby bridging the semantic gap between process mining and visual analytics. It provides a reusable theoretical framework to rigorously define visualization purposes, evaluate tool effectiveness, and support co-design of analysis systems. As a result, the interpretability and practical utility of complex compliance analysis outcomes are significantly enhanced.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Data transparency and provenance

Abstract

Conformance checking is a sub-discipline of process mining, which compares observed process traces with a process model to analyze whether the process execution conforms with or deviates from the process design. Organizations can leverage this analysis, for example to check whether their processes comply with internal or external regulations or to identify potential improvements. Gaining these insights requires suitable visualizations, which make complex results accessible and actionable. So far, however, the development of conformance checking visualizations has largely been left to tool vendors. As a result, current tools offer a wide variety of visual representations for conformance checking, but the analytical purposes they serve often remain unclear. However, without a systematic understanding of these purposes, it is difficult to evaluate the visualizations' usefulness. Such an evaluation hence requires a deeper understanding of conformance checking as an analysis domain. To this end, we propose a task taxonomy, which categorizes the tasks that can occur when conducting conformance checking analyses. This taxonomy supports researchers in determining the purpose of visualizations, specifying relevant conformance checking tasks in terms of their goal, means, constraint type, data characteristics, data target, and data cardinality. Combining concepts from process mining and visual analytics, we address researchers from both disciplines to enable and support closer collaborations.

Problem

• Classify tasks in conformance checking analyses.
• Clarify purposes of diverse conformance checking visualizations.
• Enable systematic evaluation of visualization usefulness.

Innovation

• Proposes a task taxonomy for conformance checking
• Combines process mining and visual analytics
• Categorizes tasks by goal, means, and constraints