Name
Abstract | ||
|---|---|---|
In this paper, we propose a new approach, called Query from Examples (QFE), to help non-expert database users construct SQL queries. Our approach, which is designed for users who might be unfamiliar with SQL, only requires that the user is able to determine whether a given output table is the result of his or her intended query on a given input database. To kick-start the construction of a target query Q, the user first provides a pair of inputs: a sample database D and an output table R which is the result of Q on D. As there will be many candidate queries that transform D to R, QFE winnows this collection by presenting the user with new database-result pairs that distinguish these candidates. Unlike previous approaches that use synthetic data for such pairs, QFE strives to make these distinguishing pairs as close to the original (D,R) pair as possible. By doing so, it seeks to minimize the effort needed by a user to determine if a new database-result pair is consistent with his or her desired query. We demonstrate the effectiveness and efficiency of our approach using real datasets from SQLShare, a cloud-based platform designed to help scientists utilize RDBMS technology for data analysis. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.14778/2831360.2831369 | PVLDB |
Field | DocType | Volume |
Query optimization,SQL,Data mining,Information retrieval,Computer science,Sargable,Web query classification,View,Query by Example,Spatial query,Online aggregation,Database | Journal | 8 |
Issue | ISSN | Citations |
13 | 2150-8097 | 18 |
PageRank | References | Authors |
0.68 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Li | 1 | 87 | 5.94 |
| Chee Yong Chan | 2 | 643 | 199.24 |
| David Maier | 3 | 5639 | 1666.90 |