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The TEXTURE Benchmark: Measuring Performance of Text Queries on a Relational DBMS

Summary: TEXTURE benchmarks efficiency—not relevance quality—of ranked text queries embedded in relational workloads. It uniquely mixes text and relational operators and provides a validated generator for scalable, seed-faithful synthetic text collections. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hc07a17c4b986bffa
Venue
VLDB
Year
2005
Pagerank
5.5896469e-05
Overall Rank
7,196 | 51.62%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ercegovac_vldb05,
        title = {{The TEXTURE Benchmark: Measuring Performance of Text Queries on a Relational DBMS}},
        author = {Ercegovac, Vuk and DeWitt, David J. and Ramakrishnan, Raghu},
        journal = {PVLDB},
        series = {{VLDB} '05},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
2,641 Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks 2020 SIGMOD 8.1787073e-05
3,431 Data Generation for Application-Specific Benchmarking 2011 VLDB 7.3049534e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
106 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.00033526937
1,085 On B-tree Indices for Skewed Distributions 1992 VLDB 0.00012112632
1,448 Fast Incremental Indexing for Full-Text Information Retrieval 1994 VLDB 0.00010627619
3,530 Extended User-Defined Indexing with Application to Textual Databases 1988 VLDB 7.2276073e-05
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