GraphREL: A Decomposition-Based and Selectivity-Aware Relational Framework for Processing Sub-graph Queries. Graphs are widely used for modelling complicated data such as: chemical compounds, protein interactions, XML documents and multimedia. Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications such as drug discovery and structural pattern recognition. Relational database management systems (RDBMSs) have repeatedly been shown to be able to efficiently host different types of data which were not formerly anticipated to reside within relational databases such as complex objects and XML data.The key advantages of relational database systems are its well-known maturity and its ability to scale to handle vast amounts of data very efficiently. RDMBSs derive much of their performance from sophisticated optimizer components which makes use of physical properties that are specific to the relational model such as: sortedness, proper join ordering and powerful indexing mechanisms. In this paper, we study the problem of indexing and querying graph databases using the relational infrastructure. We propose a novel, decomposition-based and selectivity-aware SQL translation mechanism of sub-graph search queries. Moreover, we carefully exploit existing database functionality such as partitioned B-trees indexes and influencing the relational query optimizers by selectivity annotations to reduce the access costs of the secondary storage to a minimum. Finally, our experiments utilise an IBM DB2 RDBMS as a concrete example to confirm that relational database systems can be used as an efficient and very scalable processor for sub-graph queries

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  1. Lee, Chun-Hee; Chung, Chin-Wan: Efficient search in graph databases using cross filtering (2014)
  2. Sakr, Sherif: Graphrel: A decomposition-based and selectivity-aware relational framework for processing sub-graph queries (2009) ioport