Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich variety of techniques, each often targeting a specific type of errors, e.g., numeric outliers, constraint violations, etc. While these diverse techniques clearly improve data quality, it places a significant burden on humans to configure these techniques with suitable rules and parameters for each data set. For example, an expert is expected to define suitable functional-dependencies between column pairs, or tune appropriate thresholds for outlier-detection algorithms, all of which are specific to one individual data set. As a result, users today often hire experts to cleanse only their high-value data sets.<\/p>\n
We propose Uni-Detect, a unified framework to automatically detect diverse types of errors. Our approach employs a novel “what-if” analysis that performs local data perturbations to reason about data abnormality, leveraging classical hypothesis-tests on a large corpus of tables. We test Uni-Detect on a wide variety of tables including Wikipedia tables, and make surprising discoveries of thousands of FD violations, numeric outliers, spelling mistakes, etc., with better accuracy than existing algorithms specifically designed for each type of errors. For example, for spelling mistakes, Uni-Detect outperforms the state-of-the-art spell-checker from a commercial search engine.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich variety of techniques, each often targeting a specific type of errors, e.g., numeric outliers, constraint violations, etc. While these diverse techniques clearly improve data quality, it places a significant burden on humans to configure these techniques with suitable rules […]<\/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":[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-578671","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-7-1","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":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/04\/Uni-Detect.pdf","id":"587377","title":"uni-detect","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":587377,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/05\/Uni-Detect.pdf"}],"msr-author-ordering":[{"type":"text","value":"Pei Wang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Yeye He","user_id":34992,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Yeye He"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[967218],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":967218,"post_title":"Self-service Data Preparation","post_name":"self-service-data-preparation","post_type":"msr-project","post_date":"2023-11-08 14:36:00","post_modified":"2023-11-18 10:15:39","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/self-service-data-preparation\/","post_excerpt":"It is often cited that data scientists spend a significant portion of their time (up to 80%), cleaning and preparing data. For less-technical users, who may be less proficient in writing code (e.g., in Excel, Power-BI and Tableau), the tasks of preparing and cleaning data are not just time-consuming, but also technically challenging. 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