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A big data analytics framework for scientific data management

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The Ophidia project is a research effort addressing big data analytics requirements, issues, and challenges for eScience. We present here the Ophidia analytics framework, which is responsible for atomically processing, transforming and manipulating array-based data. This framework provides a common...

A Comparison of Approaches to Large-Scale Data Analysis

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A Comparison of Approaches to Large-Scale Data Analysis

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There is currently considerable enthusiasm around the MapReduce(MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in  parallel SQL database management systems (DBMS) for over 20 years, some have called MR a...

A Comprehensive View of Hadoop MapReduce Scheduling Algorithms

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Hadoop is a Java-based programming framework that supports the storing and processing of large data sets in a distributed computing environment and it is very much appropriate for high volume of data. it's using HDFS for data storing and using...

A genetic algorithm-based job scheduling model for big data analytics

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Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the...

A Secure Cloud Computing Based Framework for Big Data Information Management of Smart Grid

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Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end...

A Unified MapReduce Domain-Specific Language for Distributed and Shared Memory Architectures

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MapReduce is a suitable and ecient parallel programming pattern for processing big data analysis. In recent years, many frameworks/languages have implemented this pattern to achieve high performance in data mining applications, particularly for distributed memory architectures (e.g., clusters).Nevertheless, the industry...

AI2 : Training a big data machine to defend

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AI2 : Training a big data machine to defend

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We present an analyst-in-the-loop security system, where analyst intuition is put together with stateof- the-art machine learning to build an end-to-end active learning system. The system has four key features: a big data behavioral analytics platform, an ensemble of outlier...

An Economic-based Resource Management and Scheduling for Grid Computing Applications

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Resource management and scheduling plays a crucial role in achieving high utilization of resources in grid computingenvironments. Due to  heterogeneity of resources, scheduling an application is significantly complicated and challenging task in grid system. Most of the researches in this...

Bayes and Big Data: The Consensus Monte Carlo Algorithm

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Bayes and Big Data: The Consensus Monte Carlo Algorithm

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A useful de nition of \big data" is data that is too big to comfortably process on a single machine, either because of processor, memory, or disk bottlenecks. Graphics processing units can alleviate the processor bottleneck, but memory or disk bottlenecks...
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