SPARK: Top-k Keyword Query in Relational Databases
Summary: Introduces SPARK for top-k keyword queries over relational data using a virtual-document ranking that adapts IR techniques. Proposes efficient processing with minimal DB accesses; shows strong effectiveness and efficiency on large real datasets across two RDBMS. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yi Luo
- 2. Xuemin Lin
- 3. Wei Wang
- 4. Xiaofang Zhou
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,564 | Keyword Search in Databases: The Power of RDBMS | 2009 | SIGMOD | 0.00011350495 |
| 3,758 | Keyword Search over Relational Databases: A Metadata Approach | 2011 | SIGMOD | 6.7824746e-05 |
| 427 | Automated Ranking of Database Query Results | 2003 | CIDR | 0.0002352637 |
| 6,050 | Ranking Objects by Exploiting Relationships: Computing Top-K over Aggregation | 2006 | SIGMOD | 5.2360729e-05 |
| 8,766 | Toward Scalable Keyword Search over Relational Data | 2010 | VLDB | 4.456315e-05 |
| 9,589 | A Graph Method for Keyword-based Selection of the top-K Databases | 2008 | SIGMOD | 4.3209273e-05 |
| 9,069 | Keyword Querying and Ranking in Databases | 2009 | VLDB | 4.4032906e-05 |
| 5,672 | Effective Keyword-based Selection of Relational Databases | 2007 | SIGMOD | 5.3784128e-05 |
| 276 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029336949 |
| 877 | Effective Keyword Search in Relational Databases | 2006 | SIGMOD | 0.00015714014 |