Hybrid partitioning and distribution of RDF data

Abstract

quotRDF is a standard model by W3C specifically designed for data interchange on the web. RDF was established and used for the development of the semantic web. However, nowadays RDF data is being used for diverse domains and is not limited to the semantic web. Tremendous increase is witnessed in RDF data due to its applications in various domains. With growing RDF data it is vital to manage this data efficiently. The thesis aims at efficient storage and faster querying of RDF data using various data partitioning techniques. newline newlineThe thesis studies the problem of basic data partitioning techniques for RDF data storage and proposes the use of hybrid data partitioning in centralized and distributed environment as a part of the solution to store and query RDF data. The dissertation emphasizes on efficient data storage and faster query execution for stationary RDF data. It demonstrates basic data partitioning techniques like PT (Property Table), BT (Binary Table), HP (Horizontally Partitioned Table), and use of MV (Materialized Views) over BT. Even though basic data partitioning techniques outperforms TT (Triple Table) they suffer from various performances issues. newline newlineThe thesis gives a detailed insight into advantages and disadvantages of basic data partitioning techniques. Consequently, it proposes hybrid solutions for data partitioning by exploiting the best of available techniques. It proposes three hybrid data partitioning techniques namely DAHP (Data-Aware Hybrid Partitioning), DASIVP (Data-Aware Structure Indexed Vertical Partitioning) and WAHP (Workload-Aware Hybrid Partitioning). DAHP and WAHP are a combination of PT and BT whereas DASIVP combines structure index partitioning with BT. DAHP and DASIVP consider a data-aware approach and WAHP considers a workload-aware approach. Data-aware approach stores RDF data based on how the data is related to each other in the dataset and workload-aware approach stores RDF data based on how the data that is queried together.