Automated Ranking of Database Query Results
Summary: Adapts IR-style relevance ranking to relational DB query results, highlighting integration challenges in scoring, indexing, query optimization, and result diversification for DBMS. Proposes and evaluates several IR-to-DB adaptations and reports preliminary experimental tradeoffs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sanjay Agrawal
- 2. Surajit Chaudhuri
- 3. Gautam Das
- 4. Aristides Gionis
Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 1,201 | SPARK: Top-k Keyword Query in Relational Databases | 2007 | SIGMOD | 0.0001334371 |
| 877 | Effective Keyword Search in Relational Databases | 2006 | SIGMOD | 0.00015714014 |
| 12,088 | Rank Discovery From Web Databases | 2013 | VLDB | 4.1945683e-05 |
| 276 | Efficient IR-Style Keyword Search over Relational Databases | 2003 | VLDB | 0.00029336949 |
| 1,609 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00011150935 |
| 1,616 | Relational link-based ranking | 2004 | VLDB | 0.00011128652 |
| 11,883 | Query Reranking As A Service | 2016 | VLDB | 4.1945683e-05 |
| 1,258 | Ordering the Attributes of Query Results | 2006 | SIGMOD | 0.00013013676 |
| 9,069 | Keyword Querying and Ranking in Databases | 2009 | VLDB | 4.4032906e-05 |
| 1,992 | Probabilistic Ranking of Database Query Results | 2004 | VLDB | 9.8462684e-05 |