Amoeba: A Shape changing Storage System for Big Data
Summary: Amoeba provides robust multi-attribute partitioning for ad-hoc queries without a known workload. It continually repartitions from observed queries, balancing adaptivity to evolving workloads against robustness and avoiding performance spikes. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anil Shanbhag (Massachusetts Institute of Technology)
- 2. Alekh Jindal (Microsoft)
- 3. Yi Lu (Massachusetts Institute of Technology)
- 4. Samuel Madden (Massachusetts Institute of Technology)
BibTeX Citation
@article{shanbhag_vldb16,
title = {{Amoeba: A Shape changing Storage System for Big Data}},
author = {Shanbhag, Anil and Jindal, Alekh and Lu, Yi and Madden, Samuel},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
doi = {10.14778/3007263.3007311},
url = {https://doi.org/10.14778/3007263.3007311},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,073 | Pushing Data-Induced Predicates Through Joins in Big-Data Clusters | 2020 | VLDB | 7.6777283e-05 |
| 4,533 | AdaptDB: Adaptive Partitioning for Distributed Joins | 2017 | VLDB | 6.5565658e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 187 | DB2 Design Advisor: Integrated Automatic Physical Database Design | 2004 | VLDB | 0.0002592488 |
| 253 | Database Cracking | 2007 | CIDR | 0.00023042111 |
| 1,036 | Fine-grained Partitioning for Aggressive Data Skipping | 2014 | SIGMOD | 0.00012377471 |
| 1,694 | Self-organizing Tuple Reconstruction in Column-stores | 2009 | SIGMOD | 9.8562172e-05 |
| 1,883 | Automated Partitioning Design in Parallel Database Systems | 2011 | SIGMOD | 9.4391795e-05 |
| 5,526 | AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data | 2015 | VLDB | 6.0937354e-05 |
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