HaLoop

By Yingyi Bu, Bill Howe, Magdalena Balazinska, Michael D. Ernst. Proceedings of the VLDB Endowment, 2010

886 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/8a3c09414e8037dcf61a243af3f1c71e5c9a9ea8

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

WTF: the who to follow service at Twitter (2013, positive): “Most of these issues can be addressed in a more principled way by extending the MapReduce programming model, for example, HaLoop [3], Twister [7], and PrIter [31].”
Exploiting iterative-ness for parallel ML computations (2014, methodology): “Some rely on repeated executions of MapReduce for iterations [7, 12].”
SkewTune: mitigating skew in mapreduce applications (2012, background): “Instead developers rely on “tricks” to achieve high performance: ordering properties of intermediate results, custom partitioning functions, extensions to support pipelining [34] and iteration [5], and assumptions about the number of partitions.”
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads (2012, background): “Consequently, recent studies on query-like programming extensions for MapReduce [14,27,49] and applying query optimization techniques to MapReduce [16,23,26,31,34,43] are likely to bring considerable benefit.”
A survey on platforms for big data analytics (2014, background): “One such work called HaLoop [15] extends MapReduce with programming support for iterative algorithms and improves efficiency by adding caching mechanisms.”
Big Data Processing in Cloud Computing Environments (2012, neutral): “HaLoop[34] is similar to Twister, which is a modified version of the MapReduce framework that supports for iterative applications by adding a Loop control.”
Big data analytics on Apache Spark (2016, positive): “, HaLoop [13] and Twister [26]), GraphLab [56] PowerGraph [34], Google’s Pregel [58] (a Pregel API was implemented on top of Spark core [94]) and of course Spark’s MLlib [61].”
Dandelion: a compiler and runtime for heterogeneous systems (2013, background): “Extending dataflow systems to support iteration [21, 40, 70, 75] or incremental iterative computation [72, 41, 73, 74] is an active research area.”
Shark: SQL and rich analytics at scale (2012, methodology): “Finally, Shark builds on the distributed approaches for machine learning developed in systems like Graphlab [27], Haloop [11], and Spark [39].”
Machine Learning With Big Data: Challenges and Approaches (2017, methodology): “Because MapReduce encounters difficulties when dealing with iterative algorithms, new solutions have been proposed: HaLoop [85] and Twister [86] extend Hadoop to provide better support for iterative processing.”
Shark: fast data analysis using coarse-grained distributed memory (2012, methodology): “For example, the HaLoop project at the University of Washington [13] enables the specification of cyclic workflows, while the MapReduce Online project at Berkeley [18] allows data to be piped between operators to reduce the latency of iterative jobs.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, background): “Iterative frameworks on MapReduce style computing models is an area that has been explored in Twister [18] and Haloop [13].”