TEC-MAP:评价标准分类法及其在数据和流程多重建模中的应用

IF 2 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software and Systems Modeling Pub Date : 2024-08-16 DOI:10.1007/s10270-024-01198-6
Charlotte Verbruggen, Monique Snoeck
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引用次数: 0

摘要

企业信息系统工程领域使用许多不同的概念建模语言和方法来说明开发中系统的要求。开发中系统的复杂性可能要求用不同的模型来处理不同的视角,如数据和流程视角。因此,建模者必须根据其具体的建模目标选择适当的(一组)建模语言。考虑到不同方面与单一系统相关,理想情况下,捕捉不同视角的模型应保持一致,以确保它们之间的整合。每种(套)候选建模语言都各有利弊。为了在这一问题上做出明智的选择,建模人员应选择与其问题领域相关的一些标准,并根据这些标准对候选建模语言进行比较。目前还没有一个综合建模方法的全面评估框架,它除了考虑所需的建模能力外,还考虑了可理解性、易用性、模型质量等更广泛的方面,因此是本文的重点。近年来,对建模语言的几种组合进行了研究。在这些组合中,数据+ 流程建模引起了广泛关注,有趣的是,针对这种组合的评估框架也已提出。因此,本文将主要关注数据和流程的综合多重建模,包括用户和授权的流程相关观点。本文的贡献有两个方面:在理论层面,本文概述了现有文献中的评价框架,建立了一套更完整的评价标准,并提出了一个统一的评价标准分类法(TEC-MAP);在实践层面,本文为建模者提供了指导和支持,帮助他们根据自己的问题领域选择合适的评价标准,并介绍了 TEC-MAP 的三个应用实例。
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TEC-MAP: a taxonomy of evaluation criteria and its application to the multi-modelling of data and processes

The domain of Enterprise Information Systems Engineering uses many different conceptual modelling languages and methods to specify the requirements of a system under development. The complexity of the systems under development may require addressing different perspectives with different models, such as the data and process perspectives. The modeller will thus have to choose the appropriate (set of) modelling languages according to their specific modelling goal. Given that the different aspects relate to a single system, ideally, the models that capture the different perspectives should be aligned and consistent to ensure their integration. Each candidate (set of) modelling languages comes with advantages and disadvantages. To make an informed choice in this matter, the modeller should select a number of criteria relevant to their problem domain and compare candidate modelling languages based on these criteria. A comprehensive evaluation framework for integrated modelling approaches, that considers more general aspects such as understandability, ease of use, model quality, etc. besides the ability to model the desired aspects, does not yet exist and is therefore the focus of this paper. In recent years, several combinations of modelling languages have been investigated. Amongst these combinations, data + process modelling has attracted a lot of interest, and, interestingly, evaluation frameworks for this combination have been proposed as well. Therefore, this paper will primarily focus on the integrated multi-modelling of data and processes, including the process-related viewpoints of users and authorisations. The contribution of this paper is two-fold: on a theoretical level, the paper provides an overview of existing evaluation frameworks in the literature, builds a more complete set of evaluation criteria and proposes a unified taxonomy for the classification of these evaluation criteria (TEC-MAP); on a practical level, the paper provides guidance and support to the modeller for selecting the appropriate evaluation criteria for their problem domain and presents three examples of the application of TEC-MAP.

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来源期刊
Software and Systems Modeling
Software and Systems Modeling 工程技术-计算机:软件工程
CiteScore
6.00
自引率
20.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns: Domain-specific models and modeling standards; Model-based testing techniques; Model-based simulation techniques; Formal syntax and semantics of modeling languages such as the UML; Rigorous model-based analysis; Model composition, refinement and transformation; Software Language Engineering; Modeling Languages in Science and Engineering; Language Adaptation and Composition; Metamodeling techniques; Measuring quality of models and languages; Ontological approaches to model engineering; Generating test and code artifacts from models; Model synthesis; Methodology; Model development tool environments; Modeling Cyberphysical Systems; Data intensive modeling; Derivation of explicit models from data; Case studies and experience reports with significant modeling lessons learned; Comparative analyses of modeling languages and techniques; Scientific assessment of modeling practices
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