Towards Multi-Faceted Visual Process Analytics

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2025-05-06 DOI:10.1016/j.is.2025.102560

Stef van den Elzen , Mieke Jans , Niels Martin , Femke Pieters , Christian Tominski , Maria-Cruz Villa-Uriol , Sebastiaan J. van Zelst

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引用次数: 0

Abstract

Both the fields of Process Mining (PM) and Visual Analytics (VA) aim to make complex phenomena understandable. In PM, the goal is to gain insights into the execution of complex processes by analyzing the event data that is captured in event logs. This data is inherently multi-faceted, meaning that it covers various data facets, including spatial and temporal dependencies, relations between data entities (such as cases/events), and multivariate data attributes per entity. However, the multi-faceted nature of the data has not received much attention in PM. Conversely, VA research has investigated interactive visual methods for making multi-faceted data understandable for about two decades. In this study, we bring together PM and VA with the goal of advancing towards Visual Process Analytics (VPA) of multi-faceted processes. To this end, we present a systematic view of relevant (VA) data facets in the context of PM and assess to what extent existing PM visualizations address the data facets’ characteristics, making use of VA guidelines. In addition to visualizations, we look at how PM can benefit from analytical abstraction and interaction techniques known in the VA realm. Based on this, we discuss open challenges and opportunities for future research towards multi-faceted VPA.

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面向多面可视化过程分析

过程挖掘（Process Mining， PM）和可视化分析（Visual Analytics， VA）都旨在使复杂的现象变得可理解。在PM中，目标是通过分析事件日志中捕获的事件数据来深入了解复杂流程的执行情况。该数据本质上是多方面的，这意味着它涵盖了各种数据方面，包括空间和时间依赖性、数据实体（如案例/事件）之间的关系以及每个实体的多变量数据属性。然而，数据的多面性在项目管理中并没有得到太多的关注。相反，VA的研究已经研究了交互可视化方法，使多方面的数据可以理解，大约有二十年了。在本研究中，我们将PM和VA结合在一起，目标是推进多面过程的可视化过程分析（VPA）。为此，我们提出了项目管理背景下相关（VA）数据方面的系统视图，并评估现有项目管理可视化处理数据方面特征的程度，利用VA指南。除了可视化之外，我们还将了解PM如何从VA领域中已知的分析抽象和交互技术中获益。在此基础上，我们讨论了面向多方面的VPA未来研究面临的挑战和机遇。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.