{"id":155360,"date":"2008-06-01T00:00:00","date_gmt":"2008-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/transformation-based-framework-for-record-matching\/"},"modified":"2018-10-16T19:56:10","modified_gmt":"2018-10-17T02:56:10","slug":"transformation-based-framework-for-record-matching","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/transformation-based-framework-for-record-matching\/","title":{"rendered":"Transformation-based Framework for Record Matching"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Today&#8217;s record matching infrastructure does not allow a flexible way to account for synonyms such as &#8220;Robert&#8221; and &#8220;Bob&#8221; which refer to the same name, and more general forms of string transformations such as abbreviations. We propose a programmatic framework of record matching that takes such user-defined string transformations as input. To the best of our knowledge, this is the first proposal for such a framework. This transformational framework, while expressive, poses significant computational challenges which we address. We empirically evaluate our techniques over real data.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today&#8217;s record matching infrastructure does not allow a flexible way to account for synonyms such as &#8220;Robert&#8221; and &#8220;Bob&#8221; which refer to the same name, and more general forms of string transformations such as abbreviations. We propose a programmatic framework of record matching that takes such user-defined string transformations as input. To the best of [&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":[{"type":"user_nicename","value":"arvinda"},{"type":"user_nicename","value":"surajitc"},{"type":"user_nicename","value":"skaushi"}],"msr_publishername":"IEEE Computer Society","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 24th International Conference on Data Engineering, ICDE 2008","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":"Copyright \u00a9 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. 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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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