{"id":162564,"date":"2012-04-01T00:00:00","date_gmt":"2012-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/horton-online-query-execution-engine-for-large-distributed-graphs-demo-track\/"},"modified":"2018-10-16T20:30:11","modified_gmt":"2018-10-17T03:30:11","slug":"horton-online-query-execution-engine-for-large-distributed-graphs-demo-track","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/horton-online-query-execution-engine-for-large-distributed-graphs-demo-track\/","title":{"rendered":"Horton: Online Query Execution Engine for Large Distributed Graphs (Demo Track)"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Large graphs are used in many applications, such as social networking. The management of these graphs poses new challenges because such graphs are too large to fit on a single server. Current distributed techniques such as map-reduce and Pregel are not well-suited for processing interactive adhoc 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 expressing regular language reachability 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, in which a graph models data on software components, developers, development artifacts (such as bug reports), and their interactions in large software projects.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large graphs are used in many applications, such as social networking. The management of these graphs poses new challenges because such graphs are too large to fit on a single server. Current distributed techniques such as map-reduce and Pregel are not well-suited for processing interactive adhoc queries against large graphs. In this paper we demonstrate [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162564","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"ICDE 2012: 28th IEEE International Conference on Data 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