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 | 0.00011340407 |
| 3,766 | Keyword Search over Relational Databases: A Metadata Approach | 2011 | SIGMOD | 6.7753484e-05 |
| 427 | Automated Ranking of Database Query Results | 2003 | CIDR | 0.0002350378 |
| 6,054 | Ranking Objects by Exploiting Relationships: Computing Top-K over Aggregation | 2006 | SIGMOD | 5.2310615e-05 |
| 8,763 | Toward Scalable Keyword Search over Relational Data | 2010 | VLDB | 4.4520434e-05 |
| 9,589 | A Graph Method for Keyword-based Selection of the top-K Databases | 2008 | SIGMOD | 4.3167828e-05 |
| 9,065 | Keyword Querying and Ranking in Databases | 2009 | VLDB | 4.3992275e-05 |
| 5,683 | Effective Keyword-based Selection of Relational Databases | 2007 | SIGMOD | 5.3733855e-05 |
| 278 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029322862 |
| 873 | Effective Keyword Search in Relational Databases | 2006 | SIGMOD | 0.0001570125 |