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
• 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

• Terminal Agents: A Survey of AI Agents in Command-Line Environments [102.48222742145474]
  Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, tool use, and computer-use research.<n>We regard terminal agents as systems whose dominant progress-bearing action--observation loop is mediated by terminal command execution, textual feedback, and stateful environment interaction.<n>Our synthesis shows that realized behavior is jointly shaped by the model, interface, harness, runtime, and environment.
  arXiv  Detail & Related papers  (2026-08-20T18:16:44Z)
• Execution-First Synthetic Tool-Use Trace Generation for LLM Agents [10.291533584425252]
  SyntheticAgentTraceQA is an execution- first framework for generating scalable supervision data for tool- augmented agents.<n>We evaluate the framework across four tool ecosystems and use the resulting data to fine-tune and evaluate Qwen model variants.
  arXiv  Detail & Related papers  (2026-07-31T08:52:26Z)
• From Question Answering to Task Completion: A Survey on Agent System and Harness Design [67.8241530947325]
  This survey examines LLM-based agents through a model-harness lens.<n>We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model and an execution harness.<n>We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities.
  arXiv  Detail & Related papers  (2026-06-14T05:40:16Z)
• Unsupervised Skill Discovery for Agentic Data Analysis [55.42853694128929]
  DataCOPE is an unsupervised verifier-guided skill discovery framework for data-analytic agents.<n>It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation.<n>We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep.
  arXiv  Detail & Related papers  (2026-06-04T17:20:47Z)
• 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.