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SH2O: Efficient Data Access for Work-Sharing Databases
Summary: SH2O replaces shared filters with multidimensional range access patterns to reduce bottlenecks in highly concurrent work-sharing databases. Three-tier design: spatial indices, a range-optimizer, and partitioned, independent access with tuning—yields 1.8–22.2× speedups on hundreds of queries.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6723
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.1945683e-05
- Overall Rank
- 11,212 | 22.01%
- DOI
-
10.1145/3617340
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Incoming Citations (Sorted by Pagerank)
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 2 |
R-Trees: A Dynamic Index Structure For Spatial Searching |
1984 |
SIGMOD |
0.0032169493 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 185 |
DuckDB: an Embeddable Analytical Database |
2019 |
SIGMOD |
0.00036538405 |
| 244 |
Continuously Adaptive Continuous Queries over Streams |
2002 |
SIGMOD |
0.00031066222 |
| 368 |
Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing |
1998 |
VLDB |
0.000254931 |
| 497 |
Column-Stores vs. Row-Stores: How Different Are They Really? |
2008 |
SIGMOD |
0.00021716559 |
| 515 |
QPipe: A Simultaneously Pipelined Relational Query Engine |
2005 |
SIGMOD |
0.00021214633 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 830 |
Main-Memory Scan Sharing For Multi-Core CPUs |
2008 |
VLDB |
0.00016171897 |
| 940 |
SharedDB: Killing One Thousand Queries With One Stone |
2012 |
VLDB |
0.00015173166 |
| 1,026 |
Cooperative Scans: Dynamic Bandwidth Sharing in a DBMS |
2007 |
VLDB |
0.00014589172 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,299 |
The DataPath System: A Data-Centric Analytic Processing Engine for Large Data Warehouses |
2010 |
SIGMOD |
0.00012751522 |
| 1,429 |
A Scalable, Predictable Join Operator for Highly Concurrent Data Warehouses |
2009 |
VLDB |
0.00012033518 |
| 1,477 |
Fine-grained Partitioning for Aggressive Data Skipping |
2014 |
SIGMOD |
0.00011770865 |
| 1,611 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011147324 |
| 1,943 |
Procella: Unifying serving and analytical data at YouTube |
2019 |
VLDB |
0.00010012569 |
| 2,372 |
Predictable Performance for Unpredictable Workloads |
2009 |
VLDB |
8.947963e-05 |
| 3,737 |
Skipping-oriented Partitioning for Columnar Layouts |
2017 |
VLDB |
6.8033227e-05 |
| 3,922 |
Pushing Data-Induced Predicates Through Joins in Big-Data Clusters |
2020 |
VLDB |
6.6291079e-05 |
| 4,161 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
6.3938006e-05 |
| 5,293 |
MQJoin: Efficient Shared Execution of Main-Memory Joins |
2016 |
VLDB |
5.5815698e-05 |
| 6,060 |
OLTPShare: The Case for Sharing in OLTP Workloads |
2018 |
VLDB |
5.2305257e-05 |
| 7,128 |
Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning |
2021 |
SIGMOD |
4.8230171e-05 |
| 7,461 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
4.723898e-05 |
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