Open Access

ARTICLE

An Improved Distributed Query for Large-Scale RDF Data

Aoran Li¹, Xinmeng Wang¹, Xueliang Wang⁴, Bohan Li^1,2,3,*

1 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
2 Key Laboratory of Safety-Critical Software, Ministry of Industry and Information Technology, Nanjing, 211106, China
3 Collaborative Innovation Center of Novel Software Technology and Industrialization, Suzhou, 215000, China
4 School of Data Science and Technology, Heilongjiang University, Harbin, 150080, China

* Corresponding Author: Bohan Li. Email:

Journal on Big Data 2020, 2(4), 157-166. https://doi.org/10.32604/jbd.2020.010358

Received 20 April 2020; Accepted 24 August 2020; Issue published 24 December 2020

Abstract

The rigid structure of the traditional relational database leads to data redundancy, which seriously affects the efficiency of the data query and cannot effectively manage massive data. To solve this problem, we use distributed storage and parallel computing technology to query RDF data. In order to achieve efficient storage and retrieval of large-scale RDF data, we combine the respective advantage of the storage model of the relational database and the distributed query. To overcome the disadvantages of storing and querying RDF data, we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform. We conduct the LUBM query statements respectively with the selected data sets. In experiments, we compare query response time in different conditions to evaluate the feasibility and correctness of our approaches. The results show that the proposed scheme can reduce the storage cost and improve query efficiency.

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Keywords

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