Poster – Paper 545

Hash Tree Indexing for Fast SPARQL Query in Large Scale RDF Data Management Systems Wenwen Li, Bingyi Zhang, Guozheng Rao, Renhai Chen and Zhiyong Feng Poster October 23, 2017, Poster and Demo Reception, 18:30-21:20 Festsaal 1 Download paper (preprint)

Abstract

In the past decade, the volume of RDF (Resource Description Framework, which is a standard model for data interchange on the Web) data grows enormously, and many RDF datasets (e.g., Wikipedia) have reached up to billions of triples. As a result, how to efficiently manage this huge RDF data has become a tremendous challenge. In this paper, we present HTStore, a hash tree based system for fast storing and accessing large scale RDF data. The design of HTStore has three salient features. First, the compact design can effectively reduce the size of the indexes. Second, HTStore utilizes the hash function to significantly reduce the query time. Third, the proposed hash tree structure can easily adapt to the changes in data volume (e.g., data expansion). The experimental results demonstrate that the proposed system can improve the query efficiency up to 21.3% compared with the representative RDF data management systems.