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SnipSuggest: Context-Aware Autocompletion for SQL Nodira Khoussainova, YongChul Kwon, Magdalena Balazinska, and Dan Suciu Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA {nodira
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Document Date: 2010-11-09 07:48:04

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File Size: 705,59 KB

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City

Seattle / Boston / /

Company

MicroStrategy / IMDB / Addison-Wesley Longman Publishing / The star / Microsoft / Cognos / /

Currency

USD / /

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Facility

Aqua Data Studio / University of Washington eScience Institute / University of Washington / /

IndustryTerm

equivalent solution / possible approximation algorithm / search box / database systems / approximation algorithm / keyword search logs / visual query building tools / keyword search / similar tool / web query refinement / database management systems / greedy algorithm / web forms / web search / search queries / Visual query systems / Internet Movie Database / online survey / highperformance computing / /

Organization

University of Washington eScience Institute / National Science Foundation / VLDB Endowment / University of Washington / Seattle / Dan Suciu Department of Computer Science and Engineering / /

Person

Dan Suciu / Joe / FSOM FIN / Anna / G. Das / V / Magdalena Balazinska / /

Position

Astronomer / author / Query Logger / DB Usage Query Logger / /

Product

Pentax K-x Digital Camera / Canon FS10 Camcorder / /

ProgrammingLanguage

SQL / Hoc / /

ProvinceOrState

Washington / S. K. / Massachusetts / /

PublishedMedium

Journal of the ACM / /

Technology

possible approximation algorithm / API / SSCoverage algorithms / SSCoverage algorithm / machine learning / DBMS / two algorithms / approximation algorithm / greedy algorithm / Popularity-based algorithm / SSAccuracy algorithm / 2.2 Context-Aware Algorithms / above two algorithms / /

URL

http /

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