{"id":238233,"date":"2012-06-01T00:00:00","date_gmt":"2012-06-01T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/experiences-with-using-data-cleaning-technology-for-bing-services\/"},"modified":"2018-10-16T22:02:25","modified_gmt":"2018-10-17T05:02:25","slug":"experiences-with-using-data-cleaning-technology-for-bing-services","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/experiences-with-using-data-cleaning-technology-for-bing-services\/","title":{"rendered":"Experiences with using Data Cleaning Technology for Bing Services"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Over the past few years, our Data Management, Exploration and Mining (DMX) group at Microsoft Research has worked closely with the Bing team to address challenging data cleaning and approximate matching problems. In this article we describe some of the key Big Data challenges in the context of these Bing services primarily focusing on two key services: Bing Maps and Bing Shopping. We describe ideas that proved crucial in helping meet the quality, performance and scalability goals demanded by these services. We also briefly reflect on the lessons learned and comment on opportunities for future work in data cleaning technology for Big Data.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Over the past few years, our Data Management, Exploration and Mining (DMX) group at Microsoft Research has worked closely with the Bing team to address challenging data cleaning and approximate matching problems. In this article we describe some of the key Big Data challenges in the context of these Bing services primarily focusing on two [&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":[13563],"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-238233","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_edition":"Data Engineering Bulletin","msr_affiliation":"","msr_published_date":"2012-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Data Engineering Bulletin","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":"238486","msr_publicationurl":"http:\/\/sites.computer.org\/debull\/A12june\/cleaning.pdf","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"datacleaning_deb12.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/06\/datacleaning_deb12-1.pdf","id":238486,"label_id":0},{"type":"url","title":"http:\/\/sites.computer.org\/debull\/A12june\/cleaning.pdf","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/sites.computer.org\/debull\/A12june\/cleaning.pdf"},{"id":238486,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/06\/datacleaning_deb12-1.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"arvinda","user_id":31106,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=arvinda"},{"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":"user_nicename","value":"zmchen","user_id":35150,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=zmchen"},{"type":"user_nicename","value":"krisgan","user_id":32579,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=krisgan"},{"type":"user_nicename","value":"skaushi","user_id":33680,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=skaushi"},{"type":"user_nicename","value":"viveknar","user_id":34602,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=viveknar"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169513],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":169513,"post_title":"Data Cleaning","post_name":"data-cleaning","post_type":"msr-project","post_date":"2002-07-01 16:21:12","post_modified":"2017-06-06 10:55:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-cleaning\/","post_excerpt":"Poor data quality is a well-known problem in data warehouses that arises for a variety of reasons such as data entry errors and differences in data representation among data sources. For example, one source may use abbreviated state names while another source may use fully expanded state names. However, high quality data is essential for accurate data analysis. Data cleaning is the process of detecting and correcting errors and inconsistencies in data. 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