{"id":168181,"date":"2015-07-01T00:00:00","date_gmt":"2015-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/immortalgraph-a-system-for-storage-and-analysis-of-temporal-graphs\/"},"modified":"2018-10-16T21:11:37","modified_gmt":"2018-10-17T04:11:37","slug":"immortalgraph-a-system-for-storage-and-analysis-of-temporal-graphs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/immortalgraph-a-system-for-storage-and-analysis-of-temporal-graphs\/","title":{"rendered":"ImmortalGraph: A System for Storage and Analysis of Temporal Graphs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Temporal graphs that capture graph changes over time are attracting increasing interest from research communities, for functions such as understanding temporal characteristics of social interactions on a time-evolving social graph. ImmortalGraph is a storage and execution engine designed and optimized specifically for temporal graphs. Locality is at the center of ImmortalGraph\u2019s design: temporal graphs are carefully laid out in both persistent storage and memory, taking into account data locality in both time and graph-structure dimensions. ImmortalGraph introduces the notion of locality-aware batch scheduling in computation, so that common \u201cbulk\u201d operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality. The design of ImmortalGraph explores an interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. The result is a high-performance temporal-graph system that is up to 5 times more efficient than existing database solutions for graph queries. The locality optimizations in ImmortalGraph offer up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Temporal graphs that capture graph changes over time are attracting increasing interest from research communities, for functions such as understanding temporal characteristics of social interactions on a time-evolving social graph. ImmortalGraph is a storage and execution engine designed and optimized specifically for temporal graphs. Locality is at the center of ImmortalGraph\u2019s design: temporal graphs are [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168181","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM - Association for Computing Machinery","msr_edition":"ACM Transactions on Storage (TOS)","msr_affiliation":"","msr_published_date":"2015-07-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"ACM Transactions on Storage (TOS)","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":"","msr_publicationurl":"http:\/\/dl.acm.org\/citation.cfm?id=2700302&#038;dl=ACM","msr_doi":"10.1145\/2700302","msr_publication_uploader":[{"type":"url","title":"http:\/\/dl.acm.org\/citation.cfm?id=2700302&#038;dl=ACM","viewUrl":false,"id":false,"label_id":0},{"type":"doi","title":"10.1145\/2700302","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/dl.acm.org\/citation.cfm?id=2700302&#038;dl=ACM"}],"msr-author-ordering":[{"type":"user_nicename","value":"yomia","user_id":35038,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yomia"},{"type":"text","value":"Wentao Han","user_id":0,"rest_url":false},{"type":"text","value":"Kaiwei Li","user_id":0,"rest_url":false},{"type":"user_nicename","value":"miw","user_id":32960,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=miw"},{"type":"user_nicename","value":"vijayanp","user_id":34582,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=vijayanp"},{"type":"text","value":"Enhong Chen","user_id":0,"rest_url":false},{"type":"text","value":"Wenguang Chen","user_id":0,"rest_url":false},{"type":"user_nicename","value":"fanyang","user_id":31782,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=fanyang"},{"type":"user_nicename","value":"lidongz","user_id":32673,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=lidongz"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[510017,920469],"msr_project":[170955],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":170955,"post_title":"Graph Storage and Analysis","post_name":"temporal-graph-storage-and-analysis-of-social-data","post_type":"msr-project","post_date":"2012-05-17 23:28:59","post_modified":"2020-04-20 22:48:07","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/temporal-graph-storage-and-analysis-of-social-data\/","post_excerpt":"An explosion of user-generated data from online social networks motivates analysis to extract deep insights from this data's graph at scale, even of social, temporal, spatial, and topical connections. 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