业务流程模型中的行为相似性:需要更多关注的透视图

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Francesca Zampino , Laura Genga , Antonella Longo
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引用次数: 0

摘要

尽管广泛的研究已经探索了业务流程模型的相似性,但这些研究的一致性和结构仍然没有得到充分的探索。本文系统地回顾了相关文献,特别关注行为相似性。我们对过程模型相似度的文献进行了系统的回顾,重点关注两种主要的测量方法:基于跟踪和基于模型的相似度。基于99项综述研究,我们建立了一个三维框架,并对选择的相似性度量进行了定量比较,以进行更深入的分析。我们的研究结果提供了结构化的见解,以加强过程模型相似性的评估,特别是从行为的角度。审查过程遵循六个阶段的系统方法,从确定相关关键词到制作目视表示调查结果的书目图。这些见解为该领域未来的研究和实际应用奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavioral similarity in business process models: A perspective that needs more attention
Although extensive research has explored business process model similarity, the coherence and structure of these studies remain underexplored. This paper systematically reviews the literature, with a particular focus on behavioral similarity. We conduct a systematic review of the literature on process model similarity, with a focus on two primary measurement approaches: trace-based and model-based similarity. Based on 99 reviewed studies, we developed a three-dimensional framework and conducted a quantitative comparison of selected similarity measures for deeper analysis. Our findings provide structured insights to strengthen the assessment of process model similarity, particularly from a behavioral perspective. The review process follows a six-phase systematic methodology, from the identification of relevant keywords to the creation of bibliographic maps that visually represent the findings. These insights offer a foundation for future research and practical applications within the field.
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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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