SCOPE: easy and efficient parallel processing of massive data sets

By Ronnie Chaiken, Bob Jenkins, P. Larson, Bill Ramsey, Darren Shakib, Simon Weaver, Jingren Zhou. Proceedings of the VLDB Endowment, 2008

888 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/8429d29385ae410cef9a5cf6118528bbfc39a751

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Citations in the corpus

Hive - a petabyte scale data warehouse using Hadoop (2010, methodology): “Scope[14] is an SQL-like language on top of Microsoft’s proprietary Cosmos map/reduce and distributed file system.”
Efficient coflow scheduling with Varys (2014, neutral): “Note that traditional point-to-point communication is still a coﬂow with a single ﬂow.”
Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce (2013, background): “Declarative query interfaces such as Hive [32], Pig [21], and Scope [19] have brought the large scale data analysis one step closer to the common users by providing high level, easy to use programming abstractions to MapReduce.”
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations (2009, background): “In addition to PIG, there are several high-level language and environments for advanced MAPREDUCE-like systems, including SCOPE [33], Sawzall [34], and Sphere [35].”
Dremel (2010, methodology): “Other systems for parallel data processing include Scope [6] and DryadLINQ [23], and are discussed in more detail in [7].”
Mining Document Collections to Facilitate Accurate Approximate Entity Matching (2009, methodology): “In fact, many of our experiments (reported in Section 5) are performed over a large subset (of size 70TB) of web documents in a map-reduce infrastructure [8].”
Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience (2009, neutral): “That need appears to be widespread, as several other systems are emerging that also oﬀer high-level languages for Map-Reduce-like environments: DryadLINQ [20], Hive [3], Jaql [5], Sawzall [16] and Scope [6].”
A Large Scale Ranker-Based System for Search Query Spelling Correction (2010, methodology): “Optimized for Parallel Execution) programming model ( Chaiken et al., 2008 ) to train the Web scale n-gram LMs shown in Table 1. The SCOPE scripting language resembles SQL which many programmers are familiar with.”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “Microsoft has developed a distributed computing platform, called Cosmos, for storing and analyzing massive data sets.”
Fast personalized PageRank on MapReduce (2011, background): “Its effectiveness and simplicity has resulted in its implementation by different internet companies [9, 13, 5, 22], and widespread adoption for a wide range of applications [19], including large scale graph computations [15, 16].”
Hadoop++ (2010, background): “Over the past three years MapReduce has attained considerable interest from both the database and systems research community [7, 13, 23, 15, 16, 12, 3, 20, 8, 19, 22, 14, 4, 9, 6].”
Big Data: A Survey (2014, methodology): “For example, HDFS and Kosmosfs are derivatives of open source codes of GFS. Microsoft developed Cosmos [83] to support its search and advertisement business.”