A Task Taxonomy for Conformance Checking
- URL: http://arxiv.org/abs/2507.11976v1
- Date: Wed, 16 Jul 2025 07:18:29 GMT
- Title: A Task Taxonomy for Conformance Checking
- Authors: Jana-Rebecca Rehse, Michael Grohs, Finn Klessascheck, Lisa-Marie Klein, Tatiana von Landesberger, Luise Pufahl,
- Abstract summary: 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.<n>Current tools offer a wide variety of visual representations for conformance checking, but the analytical purposes they serve often remain unclear.<n>We propose a task taxonomy, which categorizes the tasks that can occur when conducting conformance checking analyses.
- Score: 0.2153887489636259
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis [0.0]
Real-world data analysis requires a consistent cognitive workflow.<n>We introduce I2I-STRADA, an agentic architecture designed to formalize this reasoning process.
arXiv Detail & Related papers (2025-07-23T18:58:42Z) - A Procedural Framework for Assessing the Desirability of Process Deviations [0.0]
This paper presents a procedural framework to guide process analysts in systematically assessing deviation desirability.<n>It provides a step-by-step approach for identifying which input factors to consider in what order to categorize deviations into mutually exclusive desirability categories.
arXiv Detail & Related papers (2025-06-13T07:24:57Z) - Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective [59.61868506896214]
We show that under standard data coverage assumptions, reinforcement learning is no more statistically difficult than through process supervision.<n>We prove that any policy's advantage function can serve as an optimal process reward model.
arXiv Detail & Related papers (2025-02-14T22:21:56Z) - Federated Conformance Checking [0.1433758865948252]
Conformance checking is a crucial aspect of process mining, where the main objective is to compare the execution of a process.<n>We propose a privacy-aware federated conformance-checking approach that allows for evaluating the correctness of overall cross-organizational process models.
arXiv Detail & Related papers (2025-01-23T11:30:13Z) - Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity.
We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
arXiv Detail & Related papers (2023-12-13T21:53:32Z) - Composite Learning for Robust and Effective Dense Predictions [81.2055761433725]
Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task.
We find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
arXiv Detail & Related papers (2022-10-13T17:59:16Z) - Visual Auditor: Interactive Visualization for Detection and
Summarization of Model Biases [18.434430375939755]
As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their deployment.
Recent research has developed algorithms for effectively identifying intersectional bias in the form of interpretable, underperforming subsets (or slices) of the data.
We propose Visual Auditor, an interactive visualization tool for auditing and summarizing model biases.
arXiv Detail & Related papers (2022-06-25T02:48:27Z) - Metrics reloaded: Recommendations for image analysis validation [59.60445111432934]
Metrics Reloaded is a comprehensive framework guiding researchers in the problem-aware selection of metrics.
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint.
Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics.
arXiv Detail & Related papers (2022-06-03T15:56:51Z) - CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
(Extended Version) [62.96267257163426]
We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework.
First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case.
Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
arXiv Detail & Related papers (2021-03-18T20:22:50Z) - Robust Learning Through Cross-Task Consistency [92.42534246652062]
We propose a broadly applicable and fully computational method for augmenting learning with Cross-Task Consistency.
We observe that learning with cross-task consistency leads to more accurate predictions and better generalization to out-of-distribution inputs.
arXiv Detail & Related papers (2020-06-07T09:24:33Z) - Generating Fact Checking Explanations [52.879658637466605]
A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process.
This paper provides the first study of how these explanations can be generated automatically based on available claim context.
Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
arXiv Detail & Related papers (2020-04-13T05:23:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.