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Representation of structured data using graphs is meaningful for applications such as road and social networks. With the increase in the size of graph databases, querying them to retrieve desired information poses challenges in terms of query representation and scalability. Independently, querying and graph partitioning have been researched in the literature. However, to the best of our knowledge, there is no effective scalable approach for querying graph databases using partitioning schemes. Also, it will be useful to analyze the quality of partitioning schemes from the query processing perspective.
In this thesis, we propose a divide and conquer approach to process queries over very large graph database using available partitioning schemes. We also identify a set of metrics to evaluate the effect of partitioning schemes on query processing. Querying over partitions requires handling answers that: i) are within the same partition, ii) span multiple partitions, and iii) requires the same partition to be used multiple times. Number of connected components in partitions and number of starting nodes of a plan in a partition may be useful for determining the starting partition and the sequence in which partitions need to be processed. Experiments on processing queries over three different graph databases (DBLP, IMDB, and Synthetic), partitioned using different partitioning schemes have been performed. Our experimental results show the correctness of the approach and provide some insights into the metrics gleaned from partitioning schemes on query processing. QP-Subdue a graph querying system developed at UTA, has been modified to process queries over partitions of a graph database.
