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Managing servers integration to realize distributed data computing framework is an important concern.
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Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework.
This increase in data volume consequently increases the need for an efficient computing framework to process this data and transform it into meaningful information.
MapReduce is a scalable parallel computing framework for big data processing.
MapReduce is the most popular parallel computing framework for big data processing which allows massive scalability across distributed computing environment.
Networks of sensors with wireless communication and computational capabilities can be used to increase the spatial resolution of data collection while providing a distributed computing framework for implementing structural health monitoring algorithms.
Our GPU/CPU heterogeneous parallel computing framework significant reduces the computational cost and facilitates 3D high-resolution imaging for large-scale seismic data.
The MapReduce computing framework is designed for distributed computing on massive data sets, and the new algorithm leverages MapReduce techniques to enable processing of graphs with billions of vertices.
The proposed method was validated on a pervasive computing framework, which included multiple mobile sensors (such as motion sensors, data-gloves, and bio-signal sensors).
SAMQA provides quality checking using a high-performance computing framework to quickly and automatically capture and report errors across population-scale data.
The obtained results show the points at which a Big Data computing framework based on Spark may be advantageous, for instance, when using a complex data mining technique or when surpassing a specific amount of data.
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