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An Implementation Approach of Big Data Computation by Mapping java Classes to MapReduce 3

An Implementation Approach of Big Data Computation by Mappin...

引用

3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Verma, Chitresh Pandey, Rajiv Amity Univ Lucknow Uttar Pradesh India

ISBN: (纸本)9789380544199

Big Data by virtue of its features in the V's and C's space calls for a processing methodology that is quite different form the process prevalent with traditional system of data processing. Hadoop one of popular framework is used for computation of Big Data. Hadoop with its associated technologies and techniques provides a arrangement for data storage in a distributed file system and further analysis of the same in a distributed mechanism through MapReduce. This paper explores the Hadoop and MapReduce implementation using java language. The paper details about the close association of java Mapper, driver and Reducer classes to a MapReduce environment.

关键词： Big Data Hadoop java driver java Mapper java Reducer MapReduce HDFS JobTracker Task Tracker

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Building Matlab Standalone Package from java for Differential Dependence Network Analysis Bioinformatics Toolkit

Building Matlab Standalone Package from Java for Differentia...

引用

作者： Lu Jin Virginia Polytechnic Institute and State University

学位级别：硕士

This thesis reports a software development effort to transplant Matlab algorithm into a Matlab license-free, platform dependent java based software. The result is almost equivalent to a direct translation of Matlab source codes into java or any other programming languages. Since compiled library is platform dependent, an MCR (Matlab Compiler Runtime environment) is required and has been developed to deploy the transplanted algorithm to end user. As the result, the implemented MCR is free to distribution and the streamline transplantation process is much simpler and more reliable than manually translation work. In addition, the implementation methodology reported here can be reused for other similar software engineering tasks. There are mainly 4 construction steps in our software package development. First, all Matlab *. m files or *. mex files associated with the algorithms of interest (to be transplanted) are gathered, and the corresponding shared library is created by the Matlab Compiler. Second, a java driver is created that will serve as the final user interface. This java based user interface will take care of all the input and output of the original Matlab algorithm, and prepare all native methods. Third, assisted by JNI, a C driver is implemented to manage the variable transfer between Matlab and java. Lastly, Matlab mbuild function is used to compile the C driver and aforementioned shared library into a dependent library, ready to be called from the standalone java interface. We use a caBIG TM (Cancer Biomedical Informatics Grid) data analytic toolkit, namely, the DDN (differential dependence network) algorithm as the testbed in the software development. The developed DDN standalone package can be used on any Matlab-supported platform with java GUI (Graphic User Interface) or command line parameter. As a caBIG TM toolkit, the DDN package can be integrated into other information systems such as Taverna or G-DOC. The major benefits provided by the proposed met

关键词： Matlab Compiler java Native Interface Differential Dependence Network C shared library C driver java driver

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Building Matlab Standalone Package from java for Differential Dependence Network Analysis Bioinformatics Toolkit

Building Matlab Standalone Package from Java for Differentia...

引用

作者： Jin, Lu Virginia Tech | University

This thesis reports a software development effort to transplant Matlab algorithm into a Matlab license-free, platform dependent java based software. The result is almost equivalent to a direct translation of Matlab source codes into java or any other programming languages. Since compiled library is platform dependent, an MCR (Matlab Compiler Runtime environment) is required and has been developed to deploy the transplanted algorithm to end user. As the result, the implemented MCR is free to distribution and the streamline transplantation process is much simpler and more reliable than manually translation work. In addition, the implementation methodology reported here can be reused for other similar software engineering tasks. There are mainly 4 construction steps in our software package development. First, all Matlab *.m files or *.mex files associated with the algorithms of interest (to be transplanted) are gathered, and the corresponding shared library is created by the Matlab Compiler. Second, a java driver is created that will serve as the final user interface. This java based user interface will take care of all the input and output of the original Matlab algorithm, and prepare all native methods. Third, assisted by JNI, a C driver is implemented to manage the variable transfer between Matlab and java. Lastly, Matlab mbuild function is used to compile the C driver and aforementioned shared library into a dependent library, ready to be called from the standalone java interface. We use a caBIGTM (Cancer Biomedical Informatics Grid) data analytic toolkit, namely, the DDN (differential dependence network) algorithm as the testbed in the software development. The developed DDN standalone package can be used on any Matlab-supported platform with java GUI (Graphic User Interface) or command line parameter. As a caBIGTM toolkit, the DDN package can be integrated into other information systems such as Taverna or G-DOC. The major benefits provided by the proposed methodo

关键词： java driver C driver Differetial Dependence Network C shared library java Native Interface Matlab Compiler Thesis

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