{"id":154365,"date":"2007-07-01T00:00:00","date_gmt":"2007-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/heavy-tailed-distributions-and-multi-keyword-queries\/"},"modified":"2018-10-16T20:38:09","modified_gmt":"2018-10-17T03:38:09","slug":"heavy-tailed-distributions-and-multi-keyword-queries","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/heavy-tailed-distributions-and-multi-keyword-queries\/","title":{"rendered":"Heavy-Tailed Distributions and Multi-Keyword Queries"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Intersecting inverted indexes is a fundamental operation for many applications in information retrieval and databases. Efficient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval applications often have convenient power-law constraints (also known as Zipf\u2019s Law and long tails) that allow us to materialize carefully chosen combinations of multi-keyword indexes, which significantly improve worst-case performance without requiring excessive storage. These multi-keyword indexes limit the number of postings accessed when computing arbitrary index intersections. Our evaluation on an e-commerce collection of 20 million products shows that the indexes of up to four arbitrary keywords can be intersected while accessing less than 20% of the postings in the largest single-keyword index.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Intersecting inverted indexes is a fundamental operation for many applications in information retrieval and databases. Efficient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval applications often have convenient power-law constraints (also known as Zipf\u2019s Law and long tails) that allow us [&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":[13555],"msr-publication-type":[193716],"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-154365","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"30th ACM SIGIR International Conference on Research & Developement on Information Retreival","msr_affiliation":"","msr_published_date":"2007-07-01","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":"208569","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"SIGIR07.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/SIGIR07.pdf","id":208569,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":208569,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/SIGIR07.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"surajitc","user_id":33764,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=surajitc"},{"type":"text","value":"Kenneth Church","user_id":0,"rest_url":false},{"type":"text","value":"Liying Sui","user_id":0,"rest_url":false},{"type":"user_nicename","value":"chrisko","user_id":31427,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chrisko"},{"type":"text","value":"Arnd Christian K\u00f6nig","user_id":0,"rest_url":false},{"type":"user_nicename","value":"church","user_id":31439,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=church"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169514],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169514,"post_title":"Data Exploration","post_name":"data-exploration","post_type":"msr-project","post_date":"2004-06-08 15:56:40","post_modified":"2017-06-06 10:57:58","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-exploration\/","post_excerpt":"This is a project area rather than a specific project. This project area focuses on novel ways to query, browse, extract, explore, mine and manage various kinds of data residing within the enterprise and on the web: structured data in relational databases, tabular data embedded in web pages, enterprise documents and spreadsheets as well as unstructured data in query logs, text documents and social media. 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