An efficient and scalable SPARQL query processing framework for big data using MapReduce and hybrid optimum load balancing

IF 2.7 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data & Knowledge Engineering Pub Date : 2023-11-01 DOI:10.1016/j.datak.2023.102239

V. Naveen Kumar , Ashok Kumar P.S.

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Abstract

The increasing RDF (Resource Description Framework) data volume requires a Hadoop platform for processing queries over large datasets. In this work, SPARQL (Simple Protocol and Rdf Query Language) queries are evaluated with Hadoop based on the objective of minimizing the number of joins through data partitioning for performing map/reduce jobs. The query evaluation time and the number of cross node joins are minimized with the proposed partitioning techniques. Extended vertical partitioning is proposed for distributed data stores based on objects’ explicit information for splitting predicates. For accessing the RDF data, hybrid monarch butterfly with beetle swarm load balancing optimization with Map-reduce (Hybrid Optimum Load Balancing) is applied. The proposed SPARQL query processing is evaluated over large RDF datasets. The proposed approach’s evaluation results are analyzed with the existing approaches, indicating the proposed framework’s efficiency. By using the proposed approach, an accuracy of 97 % is obtained.

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使用MapReduce和混合最优负载平衡的高效可扩展的SPARQL大数据查询处理框架

不断增长的RDF(资源描述框架)数据量需要一个Hadoop平台来处理对大型数据集的查询。在这项工作中，SPARQL(简单协议和Rdf查询语言)查询是基于通过执行map/reduce作业的数据分区最小化连接数量的目标与Hadoop一起评估的。所提出的分区技术最大限度地减少了查询评估时间和交叉节点连接的数量。针对分布式数据存储，提出了基于对象显式信息的扩展垂直分区，用于划分谓词。对于RDF数据的访问，采用Map-reduce (hybrid optimal load balancing，混合最优负载平衡)混合帝王蝶与甲虫群负载平衡优化。建议的SPARQL查询处理是在大型RDF数据集上进行评估的。将该方法的评价结果与现有方法进行了对比分析，表明了该框架的有效性。通过使用该方法，获得了97%的准确率。

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来源期刊

Data & Knowledge Engineering 工程技术-计算机：人工智能

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.

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