{"id":164253,"date":"2013-08-01T00:00:00","date_gmt":"2013-08-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/a-distributed-graph-engine-for-web-scale-rdf-data\/"},"modified":"2018-10-16T20:12:24","modified_gmt":"2018-10-17T03:12:24","slug":"a-distributed-graph-engine-for-web-scale-rdf-data","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/a-distributed-graph-engine-for-web-scale-rdf-data\/","title":{"rendered":"A Distributed Graph Engine for Web Scale RDF Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively.  Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing.  In this paper, we introduce Trinity.RDF, a distributed, memory-based graph engine for web scale RDF data. Instead of managing the RDF data in triple stores or as bitmap matrices, we store RDF data in its native graph form. It achieves much better (sometimes orders of magnitude better) performance for SPARQL queries than the state-of-the-art approaches. Furthermore, since the data is stored in its native graph form, the system can support other operations (e.g., random walks, reachability) on RDF graphs as well. We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"PVLDB","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"PVLDB","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Kai Zeng, Jiacheng Yang","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2013-08-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2013,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-164253","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"PVLDB","msr_affiliation":"","msr_published_date":"2013-08-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"205311","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"Trinity.RDF.pdf","viewUrl":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-content\/uploads\/2016\/02\/Trinity.RDF_.pdf","id":205311,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":205311,"url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-content\/uploads\/2016\/02\/Trinity.RDF_.pdf"}],"msr-author-ordering":[{"type":"text","value":"Kai Zeng","user_id":0,"rest_url":false},{"type":"text","value":"Jiacheng Yang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"haixunw","user_id":31951,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=haixunw"},{"type":"user_nicename","value":"binshao","user_id":31238,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=binshao"},{"type":"user_nicename","value":"zhowang","user_id":35131,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=zhowang"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[170585],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170585,"post_title":"Trinity","post_name":"trinity","post_type":"msr-project","post_date":"2010-10-30 03:41:46","post_modified":"2017-06-20 10:44:09","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/trinity\/","post_excerpt":"Trinity is a general purpose distributed graph system over a memory cloud. Memory cloud is a globally addressable, in-memory key-value store over a cluster of machines. Through the distributed in-memory storage, Trinity provides fast random data access power over a large data set. With the power of fast graph exploration and distributed parallel computing, Trinity supports both low-latency online query processing and high-throughput offline analytics on billion-node scale large graphs. 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