SnipSuggest: Context-Aware Autocompletion for SQL

In this paper, we present SnipSuggest, a system that provides onthe-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 undergraduate database class and another from the ...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Nodira Khoussainova, YongChul Kwon, Magdalena Balazinska, Dan Suciu
• Keywords: Non-expert Database Users, PVLDB 2010, Sloan Digital Sky Survey, SQL Composition Process