大型OWL数据集综合统计信息的高效计算:一种可扩展的方法

IF 4.4 4区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Enterprise Information Systems Pub Date : 2022-04-24 DOI:10.1080/17517575.2022.2062683

Heba Mohamed, S. Fathalla, Jens Lehmann, Hajira Jabeen

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引用次数: 1

摘要

计算数据集统计对于探索其结构至关重要，然而，对于大规模数据集来说，这变得具有挑战性。这有几个关键的好处，比如链接目标识别、词汇表重用、质量分析、大数据分析和覆盖率分析。在本文中，我们首次尝试开发一种分布式方法(OWLStats)来收集大规模OWL数据集的综合统计信息。OWLStats是一种分布式内存方法，用于利用Apache Spark计算OWL数据集的50个统计标准。我们已经成功地将OWLStats集成到SANSA框架中。实验结果表明，OWLStats在节点可扩展性和数据可扩展性方面都具有线性可扩展性。

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Efficient computation of comprehensive statistical information of large OWL datasets: a scalable approach

ABSTRACT Computing dataset statistics is crucial for exploring their structure, however, it becomes challenging for large-scale datasets. This has several key benefits, such as link target identification, vocabulary reuse, quality analysis, big data analytics, and coverage analysis. In this paper, we present the first attempt of developing a distributed approach (OWLStats) for collecting comprehensive statistics over large-scale OWL datasets. OWLStats is a distributed in-memory approach for computing 50 statistical criteria for OWL datasets utilizing Apache Spark. We have successfully integrated OWLStats into the SANSA framework. Experiments results prove that OWLStats is linearly scalable in terms of both node and data scalability.

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来源期刊

Enterprise Information Systems 工程技术-计算机：信息系统

CiteScore

11.00

自引率

6.80%

发文量

审稿时长

6 months

期刊介绍： Enterprise Information Systems (EIS) focusses on both the technical and applications aspects of EIS technology, and the complex and cross-disciplinary problems of enterprise integration that arise in integrating extended enterprises in a contemporary global supply chain environment. Techniques developed in mathematical science, computer science, manufacturing engineering, and operations management used in the design or operation of EIS will also be considered.