{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:36:19Z","timestamp":1764873379143},"reference-count":19,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:p>The problem of identifying and repairing data errors has been an area of persistent focus in data management research. However, while traditional data cleaning techniques can be effective at identifying several data discrepancies, they disregard the fact that many errors are<jats:italic>systematic<\/jats:italic>, inherent to the process that produces the data, and thus will keep occurring unless the root cause is identified and corrected.<\/jats:p><jats:p>In this demonstration, we will present a large-scale diagnostic framework called D<jats:sc>ata<\/jats:sc>XR<jats:sc>ay<\/jats:sc>. Like a medical X-ray that aids the diagnosis of medical conditions by revealing problems underneath the surface, D<jats:sc>ata<\/jats:sc>XR<jats:sc>ay<\/jats:sc>reveals hidden connections and common properties among data errors. Thus, in contrast to traditional cleaning methods, which treat the symptoms, our system investigates the underlying conditions that cause the errors.<\/jats:p><jats:p>The core of D<jats:sc>ata<\/jats:sc>XR<jats:sc>ay<\/jats:sc>combines an intuitive and principled cost model derived by Bayesian analysis, and an efficient, highly-parallelizable diagnostic algorithm that discovers common properties among erroneous data elements in a top-down fashion. Our system has a simple interface that allows users to load different datasets, to interactively adjust key diagnostic parameters, to explore the derived diagnoses, and to compare with solutions produced by alternative algorithms. Through this demonstration, participants will understand (1) the characteristics of good diagnoses, (2) how and why errors occur in real-world datasets, and (3) the distinctions with other related problems and approaches.<\/jats:p>","DOI":"10.14778\/2824032.2824117","type":"journal-article","created":{"date-parts":[[2015,9,16]],"date-time":"2015-09-16T12:18:17Z","timestamp":1442405897000},"page":"1984-1987","source":"Crossref","is-referenced-by-count":8,"title":["Error diagnosis and data profiling with data x-ray"],"prefix":"10.14778","volume":"8","author":[{"given":"Xiaolan","family":"Wang","sequence":"first","affiliation":[{"name":"University of Massachusetts, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Massachusetts, MA and University of Iowa, Iowa City, IA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Massachusetts, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin Luna","family":"Dong","sequence":"additional","affiliation":[{"name":"Google Inc., Mountain View, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Meliou","sequence":"additional","affiliation":[{"name":"University of Massachusetts, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,8]]},"reference":[{"issue":"1","key":"e_1_2_1_1_1","first-page":"3","article-title":"Tools for data translation and integration","volume":"22","author":"Abiteboul S.","year":"1999","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_2_1_2_1","first-page":"345","volume-title":"Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms","author":"Carr R. 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Freeway incident and weather data. http:\/\/portal.its.pdx.edu\/Portal\/index.php\/fhwa. US Department of Transportation Federal Highway Administration. Freeway incident and weather data. http:\/\/portal.its.pdx.edu\/Portal\/index.php\/fhwa."},{"key":"e_1_2_1_19_1","first-page":"1231","volume-title":"SIGMOD Conference","author":"Wang X.","year":"2015"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2824032.2824117","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T05:06:26Z","timestamp":1718082386000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2824032.2824117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8]]},"references-count":19,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2015,8]]}},"alternative-id":["10.14778\/2824032.2824117"],"URL":"https:\/\/doi.org\/10.14778\/2824032.2824117","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2015,8]]}}}