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: 12295
Venue: VLDB
Year: 2020
Pagerank: 5.4004741e-05
Overall Rank: 8,616 | 40.89%
DOI: 10.14778/3407790.3407817

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
3,907	Accelerating Approximate Aggregation Queries with Expensive Predicates	2021	VLDB	7.0278233e-05
6,206	Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing	2021	SIGMOD	5.9443409e-05
7,551	Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams	2022	VLDB	5.6006414e-05
10,865	Approximation-First Timeseries Query At Scale	2025	VLDB	5.093636e-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	0.00077458002
11	Implementing Data Cubes Efficiently	1996	SIGMOD	0.00071822821
82	Space-Efficient Online Computation of Quantile Summaries	2001	SIGMOD	0.00036378991
327	The Aqua Approximate Query Answering System	1999	SIGMOD	0.00021091539
451	Mergeable Summaries	2012	PODS	0.00018151445
567	Understanding Hierarchical Methods for Differentially Private Histograms	2013	VLDB	0.00016420715
691	Approximate Counts and Quantiles over Sliding Windows	2004	PODS	0.00014927798
1,100	Range Queries in OLAP Data Cubes	1997	SIGMOD	0.00012169143
1,242	Druid: A Real-time Analytical Data Store	2014	SIGMOD	0.00011516162
2,455	DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees	2019	VLDB	8.5552968e-05
2,747	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries	2018	VLDB	8.1711208e-05
2,878	Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation	2018	SIGMOD	8.0058242e-05
3,121	Answering Range Queries Under Local Differential Privacy	2019	VLDB	7.7357038e-05
3,366	AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics	2018	SIGMOD	7.4748604e-05
4,231	Pinot: Realtime OLAP for 530 Million Users	2018	SIGMOD	6.8177976e-05
4,405	DigitHist: a Histogram-Based Data Summary with Tight Error Bounds	2017	VLDB	6.7218674e-05
6,028	Hillview: A trillion-cell spreadsheet for big data	2019	VLDB	6.0019322e-05
6,060	Finding Global Icebergs over Distributed Data Sets	2006	PODS	5.9914746e-05
7,419	Structure-Aware Sampling: Flexible and Accurate Summarization	2011	VLDB	5.6229876e-05
8,603	Stream Frequency over Interval Queries	2019	VLDB	5.4041343e-05

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