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Amoeba: A Shape changing Storage System for Big Data

Summary: Adaptive multi-attribute partitioning enables shape-changing storage to support ad-hoc and evolving analytics workloads. Continuous repartitioning from observed query sequences yields progressive performance gains, preserving robustness to workload shifts and benchmarking against Spark. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11285
Venue
VLDB
Year
2016
Pagerank
4.2815507e-05
Overall Rank
9,801 | 31.82%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
3,922 Pushing Data-Induced Predicates Through Joins in Big-Data Clusters 2020 VLDB 6.6291079e-05
5,118 AdaptDB: Adaptive Partitioning for Distributed Joins 2017 VLDB 5.6820984e-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
258 DB2 Design Advisor: Integrated Automatic Physical Database Design 2004 VLDB 0.0003022091
408 Database Cracking 2007 CIDR 0.00023953844
1,477 Fine-grained Partitioning for Aggressive Data Skipping 2014 SIGMOD 0.00011770865
2,229 Self-organizing Tuple Reconstruction in Column-stores 2009 SIGMOD 9.2350274e-05
2,413 Automated Partitioning Design in Parallel Database Systems 2011 SIGMOD 8.8672223e-05
5,790 AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data 2015 VLDB 5.3269734e-05
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