A big data implementation based on Grid computing

Gârlaşu, Dan; Sandulescu, Virginia; Halcu, Ionela; Neculoiu, Giorgian; Grigoriu, Oana; Marinescu, Mariana; Marinescu, Viorel

doi:10.1109/roedunet.2013.6511732

Cited by 58 publications

(27 citation statements)

References 3 publications

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“…In Grid number of servers are interconnected by high speed network, which each server plays one many roles trough the grid. It will help to increase storage capability which is so important in big data [6,[11][12][13][14][15][16][17]. The Table 2 illustrate current Knowledge grid model and their focus and gap through big data community.…”

Section: Resultsmentioning

confidence: 99%

Knowledge Grid Model in Facilitating Knowledge Sharing Among Big Data Community

Hosseinioun¹

2018

Acta inform. Malays.

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Section: Resultsmentioning

confidence: 99%

Knowledge Grid Model in Facilitating Knowledge Sharing Among Big Data Community

Hosseinioun¹

2018

Acta inform. Malays.

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“…The development and maturity of cloud computing technologies has enabled exponential growth in the data that are produced, processed, stored, shared, analyzed, and visualized (Garlasu et al, 2013;Tian & Zhao, 2015). Data generation has increased drastically over the past few years, leading enterprises dealing with data management to swim in an enormous pool of data.…”

Section: Big Data and Disaster Recoverymentioning

confidence: 99%

Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data

Yang

Huang²,

Kao

et al. 2017

EURASIA J MATH SCI T

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The most dramatic factor shaping the future of higher education is Big Data and analytics. In the Big Data era, the explosive growth of massive data manipulations imposes a heavy burden on computation, storage, and communication in data centers. Increasing uncertainties in information system availability have become a daily serious problem. An appropriate evaluation and selection of the right information system disaster recovery (DR) site can ensure business continuity and investment optimization. However, most academic institutes always neglect the importance of DR. Not to mention the DR sites in the era of Big Data. Existing research results do not evaluate or select DR sites in general or those for academic Big Data applications in particular. Therefore, this research aims to establish an analytic framework for evaluating, selecting DR sites for academic Big Data. The proposed analytic framework is consisting of the Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based network process (DNP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. An empirical study based on a real Big Data DR application of an Asian high-performance computer center's evaluation and selection of DR sites for academic Big Data is used to illustrate the feasibility of the proposed framework. The analytic results can serve as a foundation for information technology administrators' strategies to reduce the performance gaps of a DR site for Big Data manipulations in general, and academic Big Data manipulations in special.

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“…Allalouf et al [24] have implemented similar mechanism of dimensional reduction over smart grid using network optimization principle. Garlasu et al [25] have investigated applicability of big data-based approaches on grid computing. Study towards developing middleware for facilitating desktop grid is carried out by Saad et al [26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Design of Sophisticated Distributed Knowledge Extraction Process on Grid Architecture

Parveen¹,

Narsimha²

2018

CAE

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With the rising demands of ubiquitous applications, the complexities associated with the data are exponentially increasing. Although, such massively generated complex data doesn't pose much challenge in storage system, but it definitely strikes a challenging problem in order to perform mining. The process of discovering the valuable knowledge becomes much challenging if a distributed architecture of grid network is considered. Therefore, the proposed system introduces a novel architecture that is capable of performing error-free distributed mining over grid networks. The significant contribution of proposed system is to apply a novel and cost effective optimization technique for simplifying the data structurization problem in distributed system that is found to normalize the existing data complexity problems. The study outcome exhibits significantly low errors and minimal computational cost in presence of peak traffic condition to prove that proposed architecture offers better mining approach in contrast to existing approaches.

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A big data implementation based on Grid computing

Cited by 58 publications

References 3 publications

Knowledge Grid Model in Facilitating Knowledge Sharing Among Big Data Community

Knowledge Grid Model in Facilitating Knowledge Sharing Among Big Data Community

Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data

A Novel Design of Sophisticated Distributed Knowledge Extraction Process on Grid Architecture

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