M2M: A simple Matlab-to-MapReduce translator for cloud computing

Zhang, Junbo; Dong, Xiang; Pan, Yi

doi:10.1109/tst.2013.6449402

Cited by 25 publications

(14 citation statements)

References 9 publications

(4 reference statements)

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“…Hence it is needed to introduce a new method for massive data processing. Map Reduce [3] is a very popular parallel programming model for cloud computing platforms, and has become an effective method for processing massive data by using a cluster of computers. X-to-Map Reduce (X is a program language) translator is a possible solution to help traditional programmers easily deploy an application to cloud systems through translating sequential codes to Map Reduce codes.…”

Section: Map Reduce For Cloud Servermentioning

confidence: 99%

“…X-to-Map Reduce (X is a program language) translator is a possible solution to help traditional programmers easily deploy an application to cloud systems through translating sequential codes to Map Reduce codes. A simple method [3] to translate Mat lab code to Map Reduce code is introduced from the following three aspects: It consists of the following three stages:…”

Section: Map Reduce For Cloud Servermentioning

confidence: 99%

See 1 more Smart Citation

Future of Social Network with Collaboration of Cloud Computing and Brain Computer Interface

Barua¹,

Mahmud²,

Dey³

et al. 2016

IJCA

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In current time, people are greatly depending on social network for many purposes like communicating, advertising, knowledge sharing etc. The needs are so high that people sometime need to stay logged in for all time. But people have other works too and it is hardly to concentrate on another work while chatting with a friend. We made a survey to get the real scenario of Facebook uses and discovered how it decreases working efficiency. Focusing on the problem a future technology based solution is proposed. The solution emphasized on brain computer interfacing (BCI) to access the social networks. Implementing nanotechnology and photonic communication technology provides the portability of the system for all time as well as less power consuming. A cloud computing platform is used for data resource and to store other necessary commands. For processing massive data in the cloud platform a map reducing formula is described shortly. The whole system acts as a single process in the human brain and gives the ability to do another work at the same time. General TermsHuman Brain Interfacing, Cloud Computing, Future Social Network KeywordsSocial network, Brain computer interface, Cloud computing, Nano technology, Mat lab to map reduce.

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Section: Map Reduce For Cloud Servermentioning

confidence: 99%

Section: Map Reduce For Cloud Servermentioning

confidence: 99%

Future of Social Network with Collaboration of Cloud Computing and Brain Computer Interface

Barua¹,

Mahmud²,

Dey³

et al. 2016

IJCA

View full text Add to dashboard Cite

show abstract

“…Tan et al [32] developed a tool, namely Kahuna, to diagnose performance problems in MapReduce systems. Zhang et al [33] developed a simple Matlab-to-MapReduce translator for cloud computing, namely M2M, for the basic numerical computations capable of translating a Matlab code with up to 100 commands to a MapReduce code in just a few seconds.…”

Section: Related Workmentioning

confidence: 99%

A cloud computing based system for cyber security management

Chen

et al. 2014

International Journal of Parallel, Emergent and Distributed Sys

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The exponential increase of cyber security has led to an ever-increasing accumulation of big network data for cyber security applications. The big data analysis for cyber security management presents challenges in data capturing, storing and processing. To address these challenges, in this paper we develop a cloud computing based system for cyber security management to fasten the analysis process of big network data. Our developed system is built on the MapReduce framework and consists of end-user devices, cloud infrastructure and a monitoring centre. To make our proposed system efficient, we introduce two key function modules of our system: data storage module and task scheduling module. We conduct the system implementation using Apache Hadoop, and our implemented system consists of data collection, data normalisation, data computation and data visualisation. Using ranking and aggregation as primitives for performing cyber security management, we conducted extensive experiments to show the effectiveness of our developed system. We also discuss how to extend our proposed system to other applications.

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“…The parallel program model Map-Reduce supports large distributed data sets on clusters of computers [1], [2], which allows for an efficient analysis of large amounts of data. As one of the most important and effective cloud computing techniques, MapReduce has become a popular computing model for cloud platforms [3]. Many cloud platforms support the MapReduce framework.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems

Zhang

Wong

Pan

et al. 2015

IEEE Trans. Knowl. Data Eng.

Self Cite

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As the volume of data grows at an unprecedented rate, large-scale data mining and knowledge discovery present a tremendous challenge. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits that rough sets can provide for data mining and related tasks, efficient computation of these approximations is vital. The recently introduced cloud computing model, MapReduce, has gained a lot of attention from the scientific community for its applicability to large-scale data analysis. In previous research, we proposed a MapReduce-based method for computing approximations in parallel, which can efficiently process complete data but fails in the case of missing (incomplete) data. To address this shortcoming, three different parallel matrix-based methods are introduced to process large-scale, incomplete data. All of them are built on MapReduce and implemented on Twister that is a lightweight MapReduce runtime system. The proposed parallel methods are then experimentally shown to be efficient for processing large-scale data.Index Terms-Rough sets, data mining, MapReduce, matrix, incomplete information systems J. Zhang is with the

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M2M: A simple Matlab-to-MapReduce translator for cloud computing

Cited by 25 publications

References 9 publications

Future of Social Network with Collaboration of Cloud Computing and Brain Computer Interface

Future of Social Network with Collaboration of Cloud Computing and Brain Computer Interface

A cloud computing based system for cyber security management

A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems

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