A Graph Method for Keyword-based Selection of the top-K Databases
Summary: G-KS uses a keyword graph to summarize each database and computes DB-query similarity to select top-K candidates for a KS query over multiple DBMSs. Experiments show G-KS outperforms prior methods in precision, recall, efficiency, space, and semantic flexibility. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,456 | Keyword Search on Structured and Semi-Structured Data | 2009 | SIGMOD | 7.0756309e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,766 | Keyword Search over Relational Databases: A Metadata Approach | 2011 | SIGMOD | 6.7753484e-05 |
| 6,085 | Answering Top-k Representative Queries on Graph Databases | 2014 | SIGMOD | 5.2165029e-05 |
| 4,593 | Keyword Search on Relational Data Streams | 2007 | SIGMOD | 6.0559961e-05 |
| 8,763 | Toward Scalable Keyword Search over Relational Data | 2010 | VLDB | 4.4520434e-05 |
| 1,202 | SPARK: Top-k Keyword Query in Relational Databases | 2007 | SIGMOD | 0.0001333126 |
| 1,564 | Keyword Search in Databases: The Power of RDBMS | 2009 | SIGMOD | 0.00011340407 |
| 278 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029322862 |
| 8,504 | Top-K Nearest Keyword Search on Large Graphs | 2013 | VLDB | 4.491551e-05 |
| 873 | Effective Keyword Search in Relational Databases | 2006 | SIGMOD | 0.0001570125 |
| 5,683 | Effective Keyword-based Selection of Relational Databases | 2007 | SIGMOD | 5.3733855e-05 |