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dc.contributor.advisorChakravarthy, Upendranath Sharma
dc.creatorGoyal, Ankur
dc.date.accessioned2016-01-26T23:17:45Z
dc.date.available2016-01-26T23:17:45Z
dc.date.created2015-12
dc.date.issued2015-12-09
dc.date.submittedDecember 2015
dc.identifier.urihttp://hdl.handle.net/10106/25449
dc.description.abstractGraphs have become one of the preferred ways to store structured data for various applications such as social network graphs, complex molecular structure, etc. Proliferation of graph databases has resulted in a growing need for effective querying methods to retrieve desired information. Querying has been widely studied in relational databases where the query optimizer finds a sequence of query execution steps (or plans) for efficient execution of the given query. Until now, most of the work on graph databases has concentrated on mining. For querying graph databases, users have to either learn a graph query language for posing their queries or use provided customized searches of specific substructures. Hence, there is a clear need for posing queries using graphs, consider alternative plans, and select a plan that can be processed efficiently on the graph database. In this thesis, we propose an approach to generate plans from a query using a cost-based approach that is tailored to the characteristics of the graph database. We collect metadata pertaining to the graph database and use cost estimates to evaluate the cost of execution of each plan. We use a branch and bound algorithm to limit the state space generated for identifying a good plan. Extensive experiments on different types of queries over two graph databases (IMDB and DBLP) are performed to validate our approach. Subdue – a graph mining algorithm has been modified to process a query plan instead of performing mining.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectQuery Optimizer, graph Databases, Querying System
dc.titleQP-SUBDUE: PROCESSING QUERIES OVER GRAPH DATABASES
dc.typeThesis
dc.date.updated2016-01-26T23:17:45Z
thesis.degree.departmentComputer Science and Engineering
thesis.degree.grantorThe University of Texas at Arlington
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Computer Science
dc.type.materialtext


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