G-SPARQL: a hybrid engine for querying large attributed graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Database research challenges and opportunities of big graph data
BNCOD'13 Proceedings of the 29th British National conference on Big Data
Proceedings of the VLDB Endowment
Hybrid query execution engine for large attributed graphs
Information Systems
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Graphs are used in many large-scale applications, such as social networking. The management of these graphs poses new challenges as such graphs are too large for a single server to manage efficiently. Current distributed techniques such as map-reduce and Pregel are not well-suited to processing interactive ad-hoc queries against large graphs. In this paper we demonstrate Horton, a distributed interactive query execution engine for large graphs. Horton defines a query language that allows the expression of regular language reach ability queries and provides a query execution engine with a query optimizer that allows interactive execution of queries on large distributed graphs in parallel. In the demo, we show the functionality of Horton managing a large graph for a social networking application called Codebook, whose graph represents data on software components, developers, development artifacts such as bug reports, and their interactions in large software projects.