Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective ofthis paper is to review existing techniques that leverage semantic web technologies to tackle challenges such as heterogeneity and changing requirements in DAS engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during DAS development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 82 papers that incorporate semantic models in engineering DASs. One of the paper’s findings is that existing models can be classified within four types of knowledge spheres: domain knowledge, analytics knowledge, services and user intentions. Another finding is to show how this knowledge is used in literature to enhance different tasks within the analytics process. We conclude our study by discussing the limitations of the existing body of research, showcasing the potential of semantic modeling to enhance DASs and presenting the possibility of leveraging ontologies for effective end-to-end DAS engineering.

中文翻译：

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工程数据分析解决方案的语义建模

数据分析解决方案（DAS）工程通常涉及从数据探索到结果表示的多个任务，这些任务适用于各种上下文和不同的数据集。基于开放世界假设的语义建模支持链接知识的灵活建模。本文的目的是回顾利用语义Web技术解决DAS工程中的挑战，例如异构性和不断变化的需求的现有技术。我们探索了这些技术的应用范围，它们使用的不同类型的语义概念以及这些概念在DAS开发过程中扮演的角色。为了收集研究的证据，我们通过识别和审阅将语义模型纳入工程DAS中的82篇论文，进行了系统的制图研究。论文的发现之一是，现有模型可以分为四种知识领域：领域知识，分析知识，服务和用户意图。另一个发现是展示如何在文献中使用此知识来增强分析过程中的不同任务。通过讨论现有研究的局限性，结束我们的研究，展示了语义建模增强DAS的潜力，并提出了利用本体进行有效的端到端DAS工程的可能性。