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SnipSuggest: Context-Aware Autocompletion for SQL Nodira Khoussainova, YongChul Kwon, Magdalena Balazinska, and Dan Suciu
 

Summary: 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, yongchul, magda, suciu}@cs.washington.edu
ABSTRACT
In this paper, we present SnipSuggest, a system that provides on-
the-go, context-aware assistance in the SQL composition process.
SnipSuggest aims to help the increasing population of non-expert
database users, who need to perform complex analysis on their
large-scale datasets, but have difficulty writing SQL queries. As a
user types a query, SnipSuggest recommends possible additions to
various clauses in the query using relevant snippets collected from
a log of past queries. SnipSuggest's current capabilities include
suggesting tables, views, and table-valued functions in the FROM
clause, columns in the SELECT clause, predicates in the WHERE
clause, columns in the GROUP BY clause, aggregates, and some
support for sub-queries. SnipSuggest adjusts its recommendations
according to the context: as the user writes more of the query, it is
able to provide more accurate suggestions.
We evaluate SnipSuggest over two query logs: one from an un-

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences