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 |
|---|---|---|---|---|
| 3,450 | Keyword Search on Structured and Semi-Structured Data | 2009 | SIGMOD | 7.0824082e-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 |
|---|---|---|---|---|
| 7,277 | Exact Top-k Nearest Keyword Search in Large Networks | 2015 | SIGMOD | 4.7794907e-05 |
| 6,080 | Answering Top-k Representative Queries on Graph Databases | 2014 | SIGMOD | 5.2214553e-05 |
| 4,592 | Keyword Search on Relational Data Streams | 2007 | SIGMOD | 6.0613645e-05 |
| 8,766 | Toward Scalable Keyword Search over Relational Data | 2010 | VLDB | 4.456315e-05 |
| 1,564 | Keyword Search in Databases: The Power of RDBMS | 2009 | SIGMOD | 0.00011350495 |
| 1,201 | SPARK: Top-k Keyword Query in Relational Databases | 2007 | SIGMOD | 0.0001334371 |
| 276 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029336949 |
| 8,505 | Top-K Nearest Keyword Search on Large Graphs | 2013 | VLDB | 4.4958064e-05 |
| 877 | Effective Keyword Search in Relational Databases | 2006 | SIGMOD | 0.00015714014 |
| 5,672 | Effective Keyword-based Selection of Relational Databases | 2007 | SIGMOD | 5.3784128e-05 |