Multiprocessor Architecture for Big Data Computing

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

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Infrastructure and Energy Conservation in Big Data Computing: A Survey

Journal of Telecommunications and Information Technology ◽

10.26636/jtit.2019.132419 ◽

2019 ◽

Vol 2 ◽

pp. 73-82

Author(s):

Ewa Niewiadomska-Szynkiewicz ◽

Michał P. Karpowicz

Keyword(s):

Big Data ◽

High Performance ◽

Physical Sciences ◽

Communication Management ◽

Data Intensive ◽

Efficient Data ◽

Software Platforms ◽

Allocation Algorithms ◽

And Storage ◽

Big Data Computing

Progress in life, physical sciences and technology depends on eﬃcient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of eﬃcient HPC infrastructures. This paper addresses the vital problem of energy-eﬃcient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

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KnowEnG: a knowledge engine for genomics

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv090 ◽

2015 ◽

Vol 22 (6) ◽

pp. 1115-1119 ◽

Cited By ~ 4

Author(s):

Saurabh Sinha ◽

Jun Song ◽

Richard Weinshilboum ◽

Victor Jongeneel ◽

Jiawei Han

Keyword(s):

Big Data ◽

National Institutes Of Health ◽

Knowledge Network ◽

Data Sets ◽

Network Mining ◽

Construction Of Knowledge ◽

Community Data ◽

Science Framework ◽

The University ◽

Big Data Computing

Abstract We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

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2021 5th International Conference on Cloud and Big Data Computing (ICCBDC)

10.1145/3481646 ◽

2021 ◽

Keyword(s):

Big Data ◽

International Conference ◽

Big Data Computing

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Big Data Analytics

Advances in Human and Social Aspects of Technology - HCI Challenges and Privacy Preservation in Big Data Security ◽

10.4018/978-1-5225-2863-0.ch006 ◽

2018 ◽

pp. 124-138

Author(s):

Sreenu G. ◽

M.A. Saleem Durai

Keyword(s):

Decision Making ◽

Big Data ◽

Real Life ◽

Big Data Analytics ◽

Speed Up ◽

Processing Techniques ◽

And Storage ◽

Big Data Computing ◽

New Algorithms ◽

Over Time

Advances in recent hardware technology have permitted to document transactions and other pieces of information of everyday life at an express pace. In addition of speed up and storage capacity, real-life perceptions tend to transform over time. However, there are so much prospective and highly functional values unseen in the vast volume of data. For this kind of applications conventional data mining is not suitable, so they should be tuned and changed or designed with new algorithms. Big data computing is inflowing to the category of most hopeful technologies that shows the way to new ways of thinking and decision making. This epoch of big data helps users to take benefit out of all available data to gain more precise systematic results or determine latent information, and then make best possible decisions. Depiction from a broad set of workloads, the author establishes a set of classifying measures based on the storage architecture, processing types, processing techniques and the tools and technologies used.

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