An interactive approach for group-based event log exploration

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tobias Fehrer , Linda Moder , Maximilian Röglinger
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引用次数: 0

Abstract

A major goal in process mining is to analyze processes to determine possible improvements. However, event logs often bear substantial complexity, posing challenges for process analysts. Consequently, analysts often split event logs into more serviceable groups. While tool support is a crucial enabler for this task, and many approaches for event log analysis are available, a gap remains regarding tools and methods for organizing and structuring event logs. To address this gap, we propose the Case Group Explorer, an approach to support event log grouping using interaction and visualization computationally. We instantiate our artifact as a software prototype and evaluate it through a competing artifact analysis, the application on several event logs, and a user study involving 13 practitioners. Thus, we contribute by creating design knowledge for event log exploration and process group analysis at the intersection of process analysis and visual analytics.
用于基于组的事件日志探索的交互式方法
流程挖掘的一个主要目标是分析流程以确定可能的改进。然而,事件日志通常具有相当大的复杂性,给流程分析人员带来了挑战。因此,分析人员经常将事件日志分成更易于服务的组。虽然工具支持是实现这一任务的关键因素,并且有许多事件日志分析方法可用,但是在组织和构建事件日志的工具和方法方面仍然存在差距。为了解决这一差距,我们提出了案例组资源管理器,这是一种使用交互和可视化计算来支持事件日志分组的方法。我们将我们的工件实例化为一个软件原型,并通过竞争的工件分析、几个事件日志上的应用程序以及涉及13个实践者的用户研究来评估它。因此,我们通过在过程分析和可视化分析的交叉点为事件日志探索和过程组分析创建设计知识来做出贡献。
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
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.
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