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An Overview of the MapReduce Model
2017Data is getting accumulated fast in various domains all over the world and the data size varies from terabytes to yottabytes. Such huge size data are known as Big Data. Extraction of meaningful information from raw data using special patterns are called Data Mining and sophisticated algorithms have been designed for this purpose.
S. Rajeswari +3 more
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Proceedings of the VLDB Endowment, 2010
MapReduce has been widely used for large-scale data analysis in the Cloud. The system is well recognized for its elastic scalability and fine-grained fault tolerance although its performance has been noted to be suboptimal in the database context. According to a recent study [19], Hadoop, an open source implementation of MapReduce, is slower than two ...
Jiang, D., Ooi, B.C., Shi, L., Wu, S.
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MapReduce has been widely used for large-scale data analysis in the Cloud. The system is well recognized for its elastic scalability and fine-grained fault tolerance although its performance has been noted to be suboptimal in the database context. According to a recent study [19], Hadoop, an open source implementation of MapReduce, is slower than two ...
Jiang, D., Ooi, B.C., Shi, L., Wu, S.
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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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2014
The computing power of modern high performance systems cannot be fully exploited using traditional parallel programming models. On the other hand, the growing demand for processing big data volumes requires a better control of the workflows, an efficient storage management, as well as a fault-tolerant runtime system. Trying to offer our proper solution
Tiberiu Rotaru +2 more
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The computing power of modern high performance systems cannot be fully exploited using traditional parallel programming models. On the other hand, the growing demand for processing big data volumes requires a better control of the workflows, an efficient storage management, as well as a fault-tolerant runtime system. Trying to offer our proper solution
Tiberiu Rotaru +2 more
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Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017
This paper provides the first attempt for a full-fledged query optimizer for MapReduce-based spatial join algorithms. The optimizer develops its own taxonomy that covers almost all possible ways of doing a spatial join for any two input datasets. The optimizer comes in two flavors; cost-based and rule-based.
Ibrahim Sabek, Mohamed F. Mokbel
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This paper provides the first attempt for a full-fledged query optimizer for MapReduce-based spatial join algorithms. The optimizer develops its own taxonomy that covers almost all possible ways of doing a spatial join for any two input datasets. The optimizer comes in two flavors; cost-based and rule-based.
Ibrahim Sabek, Mohamed F. Mokbel
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Towards Formalizing of MapReduce
2017 IEEE 3rd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2017As a powerful distributed computing model, MapReduce has been widely used in many domains to process massive amounts of data. To ensure its correctness, one of the appropriate ways is formal methods. In this paper, we will propose a formal language to model MapReduce Programs based on our previous work.
Yuxin Jing +4 more
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Correlation clustering in MapReduce
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014Correlation clustering is a basic primitive in data miner's toolkit with applications ranging from entity matching to social network analysis. The goal in correlation clustering is, given a graph with signed edges, partition the nodes into clusters to minimize the number of disagreements.
CHIERICHETTI, FLAVIO +2 more
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Implementing MapReduce with MSVL
2018This paper presents an approach to implementing MapReduce processes with Modeling Simulation and Verification Language (MSVL). This facilitates programmers not only to deal with large data sets but also to verify properties of programs in a convenient way.
Nan Zhang 0001 +4 more
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Private Searching on MapReduce
2010In this paper, a private searching protocol on MapReduce is introduced and formalized within the Mapping-Filtering-Reducing framework. The idea behind of our construction is that a map function Map is activated to generate (key, value) pairs; an intermedial filtering protocol is invoked to filter (key, value) pairs according to a query criteria; a ...
Huafei Zhu, Feng Bao 0001
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2019
MapReduce is one of the most popular programming paradigms that allows a user to process Big data sets. Our goal is to add privacy guarantees to the two standard algorithms of join computation for MapReduce: the cascade algorithm and the hypercube algorithm.
Xavier Bultel +4 more
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MapReduce is one of the most popular programming paradigms that allows a user to process Big data sets. Our goal is to add privacy guarantees to the two standard algorithms of join computation for MapReduce: the cascade algorithm and the hypercube algorithm.
Xavier Bultel +4 more
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