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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
- 12108
- Venue
- VLDB
- Year
- 2020
- Pagerank
- 4.4667395e-05
- Overall Rank
- 8,669 | 39.76%
- DOI
-
10.14778/3407790.3407817
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
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 |
| 11 |
Implementing Data Cubes Efficiently |
1996 |
SIGMOD |
0.0011695087 |
| 14 |
Online Aggregation |
1997 |
SIGMOD |
0.0010813443 |
| 126 |
Space-Efficient Online Computation of Quantile Summaries |
2001 |
SIGMOD |
0.00044753012 |
| 398 |
Mergeable Summaries |
2012 |
PODS |
0.00024383201 |
| 431 |
The Aqua Approximate Query Answering System |
1999 |
SIGMOD |
0.00023397171 |
| 714 |
Understanding Hierarchical Methods for Differentially Private Histograms |
2013 |
VLDB |
0.00017670757 |
| 846 |
Approximate Counts and Quantiles over Sliding Windows |
2004 |
PODS |
0.00015949293 |
| 1,363 |
Range Queries in OLAP Data Cubes |
1997 |
SIGMOD |
0.00012380611 |
| 1,588 |
Druid: A Real-time Analytical Data Store |
2014 |
SIGMOD |
0.00011232949 |
| 2,918 |
DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees |
2019 |
VLDB |
7.9043213e-05 |
| 2,954 |
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries |
2018 |
VLDB |
7.8218804e-05 |
| 3,273 |
Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation |
2018 |
SIGMOD |
7.2899198e-05 |
| 3,401 |
Answering Range Queries Under Local Differential Privacy |
2019 |
VLDB |
7.1343786e-05 |
| 3,944 |
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics |
2018 |
SIGMOD |
6.6056349e-05 |
| 4,766 |
Pinot: Realtime OLAP for 530 Million Users |
2018 |
SIGMOD |
5.933206e-05 |
| 4,815 |
DigitHist: a Histogram-Based Data Summary with Tight Error Bounds |
2017 |
VLDB |
5.8978716e-05 |
| 6,302 |
Hillview: A trillion-cell spreadsheet for big data |
2019 |
VLDB |
5.1166201e-05 |
| 6,426 |
Finding Global Icebergs over Distributed Data Sets |
2006 |
PODS |
5.0605823e-05 |
| 8,592 |
Stream Frequency over Interval Queries |
2019 |
VLDB |
4.4848323e-05 |
| 8,602 |
Structure-Aware Sampling: Flexible and Accurate Summarization |
2011 |
VLDB |
4.4822166e-05 |
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| 2,954 |
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries |
2018 |
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7.8218804e-05 |
| 8,639 |
Efficacious Data Cube Exploration by Semantic Summarization and Compression |
2003 |
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| 11,909 |
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Structure-Aware Sampling: Flexible and Accurate Summarization |
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VLDB |
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| 8,414 |
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2024 |
VLDB |
4.5135713e-05 |
| 2,583 |
Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee |
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SIGMOD |
8.4973431e-05 |
| 6,724 |
Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing |
2021 |
SIGMOD |
4.9449472e-05 |
| 8,521 |
Computing A Well-Representative Summary of Conjunctive Query Results |
2024 |
PODS |
4.4893996e-05 |