A universal approach for simplified redundancy-aware cross-model querying

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-09-04 DOI:10.1016/j.is.2024.102456

Pavel Koupil, Daniel Crha, Irena Holubová

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引用次数: 0

Abstract

Numerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users.

This paper deals with the most important aspect of data management — querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called MM-quecat.

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简化冗余感知跨模型查询的通用方法

随着多模型数据的出现，出现了许多挑战和悬而未决的问题。在大多数情况下，单一模型解决方案无法直接扩展，必须找到新的高效方法。此外，由于没有与组合和管理多模型相关的标准，情况对用户来说更加复杂和混乱。为了让用户掌握所有流行的模型，我们的解决方案基于多模型数据的抽象分类表示法，这种表示法可以看作是一个图。为了统一多模型数据的查询，我们让用户能够使用基于 SPARQL 的模型无关查询语言 MMQL 查询分类图。然后将查询分解并翻译成底层系统的语言。然后将中间结果合并为最终的分类结果，该结果可以任何选定的格式表达。对跨模型冗余的支持使人们能够创建不同的查询计划并选择最优计划。我们还介绍了我们的解决方案的概念验证实现，称为 MM-quecat。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.