CoopStore: Optimizing Precomputed Summaries for Aggregation

Summary: CoopStore optimizes per-segment item-frequency and quantile summaries to improve aggregation accuracy without scanning raw data. By leveraging extra memory for construction and aggregation, it yields tighter combined results than mergeable summaries, with provable worst-case guarantees and up to 25x interval and 4.5x data-cube error reduction. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: haa0348adc0729f3a
Venue: VLDB
Year: 2020
Pagerank: 5.2776273e-05
Overall Rank: 8,783 | 40.97%
DOI: 10.14778/3407790.3407817
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Edward Gan (Stanford University)
2. Peter Bailis (Stanford University)
3. Moses Charikar (Stanford University)

BibTeX Citation

@article{gan_vldb20,
        title = {{CoopStore: Optimizing Precomputed Summaries for Aggregation}},
        author = {Gan, Edward and Bailis, Peter and Charikar, Moses},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2174--2187},
        doi = {10.14778/3407790.3407817},
        url = {https://doi.org/10.14778/3407790.3407817},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,756	Accelerating Approximate Aggregation Queries with Expensive Predicates	2021	VLDB	7.0456217e-05
6,202	Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing	2021	SIGMOD	5.8494367e-05
7,707	Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams	2022	VLDB	5.4723863e-05
11,276	Approximation-First Timeseries Query At Scale	2025	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
9	Online Aggregation	1997	SIGMOD	0.00076265429
11	Implementing Data Cubes Efficiently	1996	SIGMOD	0.00071056708
83	Space-Efficient Online Computation of Quantile Summaries	2001	SIGMOD	0.00035962466
335	The Aqua Approximate Query Answering System	1999	SIGMOD	0.000206533
456	Mergeable Summaries	2012	PODS	0.00017904764
558	Understanding Hierarchical Methods for Differentially Private Histograms	2013	VLDB	0.0001646292
710	Approximate Counts and Quantiles over Sliding Windows	2004	PODS	0.00014603777
1,107	Range Queries in OLAP Data Cubes	1997	SIGMOD	0.00011988445
1,238	Druid: A Real-time Analytical Data Store	2014	SIGMOD	0.00011397949
2,434	DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees	2019	VLDB	8.4726771e-05
2,799	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries	2018	VLDB	7.9895512e-05
2,931	Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation	2018	SIGMOD	7.8378935e-05
3,177	Answering Range Queries Under Local Differential Privacy	2019	VLDB	7.5637918e-05
3,424	AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics	2018	SIGMOD	7.3084429e-05
4,172	Pinot: Realtime OLAP for 530 Million Users	2018	SIGMOD	6.7567851e-05
4,463	DigitHist: a Histogram-Based Data Summary with Tight Error Bounds	2017	VLDB	6.5861255e-05
5,930	Hillview: A trillion-cell spreadsheet for big data	2019	VLDB	5.9410245e-05
6,191	Finding Global Icebergs over Distributed Data Sets	2006	PODS	5.8543576e-05
7,563	Structure-Aware Sampling: Flexible and Accurate Summarization	2011	VLDB	5.4945028e-05
8,773	Stream Frequency over Interval Queries	2019	VLDB	5.2803988e-05

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