Semantic Models of Performance Indicators: A Systematic Survey

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-02-22 DOI:10.1145/3719291
Claudia Diamantini, Tarique Khan, Domenico Potena, Emanuele Storti
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引用次数: 0

Abstract

Performance Indicators and metrics are essential management tools. They provide synthetic objective measures to monitor the progress of a process, set objectives and assess deviations, enabling effective decision making. They can also be used for communication purposes, facilitating the sharing of objectives and results, or improving the awareness on certain phenomena, thus motivating more responsible and sustainable behaviors. Given their strategic role, it is of paramount importance, as well as challenging, to guarantee that the intended meaning of an indicator is fully shared among stakeholders, and that its implementation is aligned with the definition provided by decision makers, as this is a precondition for data quality and trustworthiness of the information system. Formal models, such as ontologies, have been long investigated in the literature to address the issues. This paper proposes a comprehensive survey on semantic approaches aimed to specify conceptual definitions of indicators and metrics, illustrating also the advantages of these formal approaches in relevant use cases and application domains.
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绩效指标的语义模型:系统调查
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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