绩效指标的语义模型:系统调查

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Claudia Diamantini, Tarique Khan, Domenico Potena, Emanuele Storti
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引用次数: 0

摘要

绩效指标和度量是必不可少的管理工具。它们提供综合的客观度量来监视过程的进展、设定目标和评估偏差,从而实现有效的决策制定。它们也可以用于交流目的,促进目标和结果的分享,或提高对某些现象的认识,从而激发更负责任和可持续的行为。鉴于其战略作用,确保利益攸关方充分共享指标的预期含义,并确保其实施与决策者提供的定义保持一致,这是至关重要的,也是具有挑战性的,因为这是数据质量和信息系统可靠性的先决条件。正式模型,如本体,已经在文献中进行了很长时间的研究,以解决这些问题。本文提出了一项全面的调查,旨在指定指标和度量的概念定义的语义方法,并说明了这些形式化方法在相关用例和应用领域中的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Models of Performance Indicators: A Systematic Survey
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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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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