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h-MapReduce: A Framework for Workload Balancing in MapReduce
2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), 2013The big data analytics community has accepted MapReduce as a programming model for processing massive data on distributed systems such as a Hadoop cluster. MapReduce has been evolving to improve its performance. We identified skewed workload among workers in the MapReduce ecosystem.
Venkata Swamy Martha +2 more
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Proceedings of the 22nd International Conference on World Wide Web, 2013
JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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Google’s MapReduce programming model — Revisited
Science of Computer Programming, 2008 Google’s MapReduce programming model serves for processing large data sets in a massively parallel manner. We deliver the first rigorous description of the model including its advancement as Google’s domain-specific language Sawzall.
Ralf Lammel
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2017 International Conference on High Performance Computing & Simulation (HPCS), 2017
Integrity constraints (ICs) such as Functional Dependencies (FDs) or Inclusion Dependencies (INDs) are commonly used in databases to check if input relations obey to certain pre-defined quality metrics. While Data-Intensive Scalable Computing (DISC) platforms such as MapReduce commonly accept as input (semi-structured) data not in relational format ...
Matteo Interlandi +3 more
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Integrity constraints (ICs) such as Functional Dependencies (FDs) or Inclusion Dependencies (INDs) are commonly used in databases to check if input relations obey to certain pre-defined quality metrics. While Data-Intensive Scalable Computing (DISC) platforms such as MapReduce commonly accept as input (semi-structured) data not in relational format ...
Matteo Interlandi +3 more
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IEEE Internet Computing, 2017
Don't throw the MapReduce baby out with the bath water! MapReduce represents a specific instance of a general class of data-parallel dataflow languages, in which computations are conceptualized as directed graphs, where vertices represent operations on records that flow along the directed edges.
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Don't throw the MapReduce baby out with the bath water! MapReduce represents a specific instance of a general class of data-parallel dataflow languages, in which computations are conceptualized as directed graphs, where vertices represent operations on records that flow along the directed edges.
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Proceedings of the 2nd IKDD Conference on Data Sciences, 2015
We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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Asynchronous Algorithms in MapReduce
2010 IEEE International Conference on Cluster Computing, 2010Asynchronous algorithms have been demonstrated to improve scalability of a variety of applications in parallel environments. Their distributed adaptations have received relatively less attention, particularly in the context of conventional execution environments and associated overheads.
Karthik Kambatla +3 more
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Inkrementelle Neuberechnungen in MapReduce
Datenbank-Spektrum, 2012Das MapReduce-Programmiermodell ermoglicht die skalierbare Analyse und Transformation groser Datenmengen. Wir stellen das auf MapReduce basierende Marimba-Framework zur einfachen Entwicklung von inkrementellen, selbstwartbaren Programmen vor, welche bei Anderung von Quelldaten eine vollstandige Wiederholung des MapReduce-Jobs vermeiden.
Johannes Schildgen +2 more
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Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model, 2010
The MapReduce framework has become a popular and powerful tool to process large datasets in parallel over a cluster of computing nodes [1]. Currently, there are many flavors of implementations of MapReduce, among which the most popular is the Hadoop implementation in Java [5].
Han Dong, Shujia Zhou, David Grove
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The MapReduce framework has become a popular and powerful tool to process large datasets in parallel over a cluster of computing nodes [1]. Currently, there are many flavors of implementations of MapReduce, among which the most popular is the Hadoop implementation in Java [5].
Han Dong, Shujia Zhou, David Grove
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2013
From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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