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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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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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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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On the performance projectability of MapReduce
4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, 2012A key challenge faced by users of public clouds today is how to request for the right amount of resources in the production datacenter that satisfies a target performance for a given cloud application. An obvious approach is to develop a performance model for a class of applications such as MapReduce.
Di Xie +2 more
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A New Approach to the Cloud-Based Heterogeneous MapReduce Placement Problem [PDF]
IEEE Transactions on Services Computing, 2016 Guaranteeing Quality of Service (QoS) with minimum computation cost is the most important objective of cloud-based MapReduce computations. Minimizing the total computation cost of cloud-based MapReduce computations is done through MapReduce placement ...
Maolin Tang
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Communications of the ACM, 2010
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.
Jeffrey Dean, Sanjay Ghemawat
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MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.
Jeffrey Dean, Sanjay Ghemawat
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2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering, 2009
As a programming model, MapReduce is implied for easier processing and generating large cluster of distributed data sets. We use CSP framework to model MapReduce system through which the parallelization of the computation and the distribution of data across multiple machines can be reflected.
Wen Su +3 more
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As a programming model, MapReduce is implied for easier processing and generating large cluster of distributed data sets. We use CSP framework to model MapReduce system through which the parallelization of the computation and the distribution of data across multiple machines can be reflected.
Wen Su +3 more
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A Survey on MapReduce Implementations
International Journal of Cloud Applications and Computing, 2016A distinguished successful platform for parallel data processing MapReduce is attracting a significant momentum from both academia and industry as the volume of data to capture, transform, and analyse grows rapidly. Although MapReduce is used in many applications to analyse large scale data sets, there is still a lot of debate among scientists and ...
Amer Al-Badarneh +2 more
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013
MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of tera-bytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine.
Yufei Tao 0001 +2 more
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MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of tera-bytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine.
Yufei Tao 0001 +2 more
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