A survey for managing temporal data in RDF

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Systems Pub Date : 2024-02-17 DOI:10.1016/j.is.2024.102368

Di Wu , Hsien-Tseng Wang , Abdullah Uz Tansel

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引用次数: 0

Abstract

The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of $< s u b j e c t p r e d i c a t e o b j e c t >$ . Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.

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用 RDF 管理时态数据的调查

互联网不仅是通信、交易和云存储的平台，也是一个巨大的知识库，人和机器都可以在这里创建、操作、推断和利用数据和知识。开发语义网就是为了促进这一目的，使机器能够理解数据和知识的含义，以便用于决策。资源描述框架（Resource Description Framework，RDF）构成了语义网的基础，语义网被组织成不同的层，称为 "语义网层蛋糕"（Semantic Web Layer Cake）。不过，RDF 的基本构造是一种二元关系，格式为<主谓宾>。用 RDF 表示高阶关系需要重新量化，这可能会很麻烦。时变数据非常普遍，但仅用二元关系无法充分表示。我们详细回顾了有关用时态数据扩展 RDF 的文献，比较了表示、查询、存储、实现和评估的方法。此外，我们还简要回顾了结合时态数据从空间、概率和其他维度扩展 RDF 的方法。

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来源期刊

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.