MapReduce

By Qunying Huang. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2020

837 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4e29a85dbfa31afa78ebf3e2154580d530e1ad8a

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Citations in the corpus

Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center (2011, background): “Prominent examples include MapReduce [25], Dryad [33], MapReduce Online [24] (which supports streaming jobs), Pregel [37] (a specialized framework for graph computations), and others [36, 17, 31].”
S4: Distributed Stream Computing Platform (2010, methodology): “An example of a MapReduce online architecture built as an extension of Hadoop can be found in [3].”
In-Memory Big Data Management and Processing: A Survey (2015, background): “, Storm [53], Yahoo! S4 [52], Spark Streaming [54], MapReduce Online [282]).”
BlinkDB: queries with bounded errors and bounded response times on very large data (2012, background): “Online Aggregation (OLA) [15] and its successors [11, 19] proposed the idea of providing approximate answers which are constantly rened during query execution.”
GraphLab: A Distributed Framework for Machine Learning in the Cloud (2011, methodology): “(c) Iterative extension for MapReduce are proposed [13, 14, 15].”
Efficient Coflow Scheduling Without Prior Knowledge (2015, background): “Note that coflows in different branches of a DAG can be un- Pseudocode 2 Coflow ID Generation 1: NextCoflowID = 0 .”
Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters (2012, methodology): “MapReduce Online [5] is a streaming Hadoop runtime, but cannot compose multiple MapReduce steps into a query or recover stateful reduce tasks.”
Trends in big data analytics (2014, methodology): “To extend the applicability of MapReduce, MapReduce Online [48] supports online aggregation and continuous queries, while Kambatla et.”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, unknown): “MapReduce Online [261] is devised to support online aggregation and continuous queries.”
SkewTune: mitigating skew in mapreduce applications (2012, background): “Additionally, new MapReduce extensions strive to further facilitate pipelining during execution [25, 34, 35].”
Coflow: a networking abstraction for cluster applications (2012, background): “, Dryad [17], DryadLINQ [27], SCOPE [9], FlumeJava [10], MapReduce Online [12]); a stage can start as soon as some input is available.”