Scalable and Efficient Self-Join Processing technique in RDF data

TL;DR

This paper introduces the Recursive Mapping of Twin Tables (RMTT), a novel method for RDF data management that significantly reduces self-joins and improves query performance on large datasets.

Contribution

The paper proposes RMTT, an innovative recursive table partitioning technique that enhances RDF self-join processing efficiency and scalability over existing methods.

Findings

RMTT reduces self-joins 3-4 times compared to RDF-3X.

Experimental results show improved scalability with large RDF datasets.

RMTT outperforms traditional vertical partitioning in complex queries.

Abstract

Efficient management of RDF data plays an important role in successfully understanding and fast querying data. Although the current approaches of indexing in RDF Triples such as property tables and vertically partitioned solved many issues; however, they still suffer from the performance in the complex self-join queries and insert data in the same table. As an improvement in this paper, we propose an alternative solution to facilitate flexibility and efficiency in that queries and try to reach to the optimal solution to decrease the self-joins as much as possible, this solution based on the idea of "Recursive Mapping of Twin Tables". Our main goal of Recursive Mapping of Twin Tables (RMTT) approach is divided the main RDF Triple into two tables which have the same structure of RDF Triple and insert the RDF data recursively. Our experimental results compared the performance of join queries in vertically partitioned approach and the RMTT approach using very large RDF data, like DBLP and DBpedia datasets. Our experimental results with a number of complex submitted queries shows that our approach is highly scalable compared with RDF-3X approach and RMTT reduces the number of self-joins especially in complex queries 3-4 times than RDF-3X approach