Semantic web data management: data partitioning and query execution

Abstract

Semantic Web database is an RDF database. Due to increased use of Semantic Web in real life applications, we can find immense growth in the use of RDF databases. As there is a tremendous increase in RDF data, efficient management of this data at a larger scale, and query performance are two major concerns. RDF data can be stored using various storage techniques. The RDF data used for this experiment is FOAF dataset which is a social network data. Here we study and evaluate query performance for various storage techniques in terms of query execution time and scalability using FOAF data set. Thesis demonstrates effect of data partitioning techniques on query performance. For our experiments, we have used Triple Store, Property Tables, vertically and horizontally partitioned data store to store FOAF data. Experiments were performed to analyze query execution time for all these data stores. Partitioning techniques have been observed to make queries 168 times faster compared to Triple Stores. Materialized views are used to improve query performance further for the queries which are seen frequently for social web data. Materialized views have shown better query performance in terms of execution time which is 8 times faster than the partitioned data.