SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets
Summary: SCOPE is a declarative, extensible scripting language for data analysis on clusters, hiding parallelism from users. It provides SQL-like modeling with joins and aggregates, plus user-defined operators (extractors, processors, reducers, combiners), nesting, and stepwise plans compiled into parallel execution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ronnie Chaiken (Microsoft)
- 2. Bob Jenkins (Microsoft)
- 3. Per-Åke Larson (Microsoft)
- 4. Bill Ramsey (Microsoft)
- 5. Darren Shakib (Microsoft)
- 6. Simon Weaver (Microsoft)
- 7. Jingren Zhou (Microsoft)
BibTeX Citation
@article{chaiken_vldb08,
title = {{SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets}},
author = {Chaiken, Ronnie and Jenkins, Bob and Larson, Per-Åke and Ramsey, Bill and Shakib, Darren and Weaver, Simon and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '08},
volume = {1},
number = {1},
pages = {1265--1276},
doi = {10.14778/1454159.1454224},
url = {https://doi.org/10.14778/1454159.1454224},
year = {2008}
}Incoming Citations (Sorted by Pagerank)
Showing 39 of 89 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6 | Pig Latin: A Not-So-Foreign Language for Data Processing | 2008 | SIGMOD | 0.001052036 |
| 75 | Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters | 2007 | SIGMOD | 0.0003704106 |
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