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Computing A Well-Representative Summary of Conjunctive Query Results

Summary: Computes k-sized summaries of conjunctive query results under cohesion (k-center) and diversity. Leverages oracle-based access to avoid enumeration, yielding O(1)-approximation in near-linear time (in N, poly(k)) and a 2+ε-approximation for k-center on relational data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h7053bafeff42de8d
Venue
PODS
Year
2024
Pagerank
5.5049463e-05
Overall Rank
7,513 | 49.49%
DOI
10.1145/3695835

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{agarwal_pods24,
        address = {New York, NY, USA},
        series = {{PODS} '24},
        title = {{Computing A Well-Representative Summary of Conjunctive Query Results}},
        url = {https://dl.acm.org/doi/10.1145/3695835},
        doi = {10.1145/3695835},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Agarwal, Pankaj K. and Esmailpour, Aryan and Hu, Xiao and Sintos, Stavros and Yang, Jun},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
8,279 Subset Sampling over Joins 2026 PODS 5.3637624e-05
10,370 Faster Relational Algorithms Using Geometric Data Structures 2026 PODS 4.9793485e-05
10,393 Clustering with Set Outliers and Applications in Relational Clustering 2026 PODS 4.9793485e-05
10,396 Query Answering Under Volume-Based Diversity Functions 2026 PODS 4.9793485e-05
10,852 Fast and Private Max-Sum Diversification 2026 VLDB 4.9793485e-05
11,492 Improved Approximation Algorithms for Relational Clustering 2024 PODS 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank Cited Paper Year Venue Pagerank
57 On Random Sampling over Joins 1999 SIGMOD 0.00040108301
153 New Sampling-Based Summary Statistics for Improving Approximate Query Answers 1998 SIGMOD 0.00028633995
255 The History of Histograms (abridged) 2003 VLDB 0.00022981861
275 Optimal Histograms with Quality Guarantees 1998 VLDB 0.00022413521
456 Mergeable Summaries 2012 PODS 0.0001791284
795 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013938779
1,173 Wavelet Synopses with Error Guarantees 2002 SIGMOD 0.00011686985
1,936 Max-Sum Diversification, Monotone Submodular Functions and Dynamic Updates 2012 PODS 9.3387282e-05
3,494 On Join Sampling and the Hardness of Combinatorial Output-Sensitive Join Algorithms 2023 PODS 7.2582926e-05
3,992 Guaranteeing the O~(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins 2023 PODS 6.8685334e-05
4,642 Efficient Join Synopsis Maintenance for Data Warehouse 2020 SIGMOD 6.4898745e-05
5,989 Towards Tractability of the Diversity of Query Answers: Ultrametrics to the Rescue 2024 PODS 5.9253439e-05
6,221 Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing 2021 SIGMOD 5.8463347e-05
6,545 Ranked Enumeration of Join Queries with Projections 2022 VLDB 5.7515992e-05
7,776 Efficient Indexes for Diverse Top-k Range Queries 2020 PODS 5.4546499e-05
11,492 Improved Approximation Algorithms for Relational Clustering 2024 PODS 4.9793485e-05
11,520 Faster Algorithms for Fair Max-Min Diversification in Rd 2024 SIGMOD 4.9793485e-05
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