JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique

Abstract

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better.

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

“…The AQUA+ [25] has limitation such as due to an insufficient map-reduce engine, the sharding and filtering performance gets worse when the cluster gets larger. The existing model JQPro [30] has limitations such as the semantic data capacity is maximized with the amount of RDF datasets excessive of trillion triples in RDF repositories. So, the RDF dataset's size grows simultaneously.…”

“…The section shows the comparative analysis of proposed method with existing methods like AQUA+ [25] and join query processing (JQPro) [30] in terms of execution time. Two alternative cases are examined while analyzing the proposed method.…”

“…Table 3, the proposed method is compared to AQUA+ [25] for  ISSN: 2302-9285 case 1. Table 4, shows the comparative analysis of proposed method with JQPro [30] for case. From the results of Tables 3 and 4, it is clear that the proposed method shows enhanced performance than existing methods.…”