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COMPASS: Online Sketch-based Query Optimization for In-Memory Databases
Summary: COMPASS: online, sketch-based optimization for in-memory DBs using Fast-AGMS as a single statistics source. Push-down selections and online sketch updates; incremental sketches on the join graph improve plans and reduce runtime.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hb9e7a62adee7082b
Venue
SIGMOD
Year
2021
Pagerank
6.3188773e-05
Overall Rank
5,003 | 66.37%
DOI
10.1145/3448016.3452840
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{izenov_sigmod21,
title = {{COMPASS: Online Sketch-based Query Optimization for In-Memory Databases}},
author = {Izenov, Yesdaulet and Datta, Asoke and Rusu, Florin and Shin, Jun Hyung},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452840},
url = {https://dl.acm.org/doi/10.1145/3448016.3452840},
year = {2021}
}
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Pagerank
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Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items 2023
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JoinSketch: A Sketch Algorithm for Accurate and Unbiased Inner-Product Estimation 2023
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TreeSensing: Linearly Compressing Sketches with Flexibility 2023
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10,205
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VLDB
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10,507
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SIGMOD
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CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations 2026
SIGMOD
4.9793485e-05
11,507
Sub-optimal Join Order Identification with L1-error 2024
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4.9793485e-05
11,538
A Universal Sketch for Estimating Heavy Hitters and Per-Element Frequency Moments in Data Streams with Bounded Deletions 2024
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Outgoing Citations (Sorted by Pagerank)
Showing 26 of 26 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Rank
Cited Paper
Year
Venue
Pagerank
15
How Good Are Query Optimizers, Really? 2016
VLDB
0.00061066921
40
The Case for Learned Index Structures 2018
SIGMOD
0.00046284649
53
Eddies: Continuously Adaptive Query Processing 2000
SIGMOD
0.00040860054
85
Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019
CIDR
0.00035864347
91
On the Propagation of Errors in the Size of Join Results 1991
SIGMOD
0.0003475226
103
Selectivity Estimation Without the Attribute Value Independence Assumption 1997
VLDB
0.00033894985
145
Neo: A Learned Query Optimizer 2019
VLDB
0.0002908188
149
Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998
SIGMOD
0.00028981723
272
An Overview of Query Optimization in Relational Systems 1998
PODS
0.00022509573
402
Worst-case Optimal Join Algorithms 2012
PODS
0.00019104625
428
Tracking Join and Self-Join Sizes in Limited Storage 1999
PODS
0.0001845349
688
Cardinality Estimation Done Right: Index-Based Join Sampling 2017
CIDR
0.00014753664
692
Selectivity Estimation for Range Predicates using Lightweight Models 2019
VLDB
0.00014741011
750
Join Size Estimation Subject to Filter Conditions 2015
VLDB
0.00014265196
842
Processing Complex Aggregate Queries over Data Streams 2002
SIGMOD
0.00013540697
1,066
Sketching Streams Through the Net: Distributed Approximate Query Tracking 2005
VLDB
0.00012192801
1,465
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019
SIGMOD
0.00010576304
1,508
Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015
SIGMOD
0.00010440205
1,542
Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018
VLDB
0.00010308631
1,603
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019
SIGMOD
0.00010097649
1,891
CS2: A New Database Synopsis for Query Estimation 2013
SIGMOD
9.4184294e-05
2,172
A Black-Box Approach to Query Cardinality Estimation 2007
CIDR
8.9214171e-05
2,216
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017
VLDB
8.8177753e-05
3,271
Estimating Cardinalities with Deep Sketches 2019
SIGMOD
7.4744941e-05
3,927
Statistical Analysis of Sketch Estimators 2007
SIGMOD
6.9192361e-05
6,800
ROX: Run-time Optimization of XQueries 2009
SIGMOD
5.6792071e-05
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