Storing and Querying Graph Data Using Efficient Relational Processing Techniques

Abstract

Graphs have become increasingly used for modelling complicated data such as: chemical compounds, protein interactions and social networks. Retrieving related graphs containing a query graph from a large graph database is a fundamental performance issue in any graph-based application. Relational database management systems (RDBMSs) have repeatedly shown their success and efficiency in hosting types of data which have formerly not been anticipated to live inside relational databases such as: complex objects and XML data. The big advantages of relational database systems are its well-known maturity and its high scalability to handle vast amounts of data very efficiently. In this paper, we investigate the efficiency of different proposed schemes for storing and querying various kind of graphs using the relational infrastructure. Moreover, we investigate how existing relational query optimization techniques could be effectively utilized to improve the processing times of relational-based processing of graph queries. Finally, we have qualitatively evaluated our proposed approaches using an extensive set of experiments.