Big Data Optimization: Recent Developments and Challenges by Ali Emrouznejad

By Ali Emrouznejad

The major target of this booklet is to supply the required historical past to paintings with vast facts via introducing a few novel optimization algorithms and codes in a position to operating within the enormous information atmosphere in addition to introducing a few purposes in mammoth information optimization for either teachers and practitioners , and to profit society, undefined, academia, and executive. proposing purposes in quite a few industries, this e-book might be precious for the researchers aiming to analyses huge scale information. a number of optimization algorithms for giant info together with convergent parallel algorithms, constrained reminiscence package deal set of rules, diagonal package deal process, convergent parallel algorithms, community analytics, and lots of extra were explored during this book.

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Saracco of IBM mentions that [10]: “Even if your big data solution uses open source software, there are still expenses involved for designing, developing, deploying, and maintaining your solution. So what did your business gain from that investment? The answer to that question is going to be speciﬁc to your application and your business”. V. Zicari et al. Elastic computing in the cloud? How does it relate to Big Data Analytics? Cloud computing and virtualization of resources are directly related to big data.

However, as the CloudRank-D covers various representative applications it cannot really be compared with function-speciﬁc benchmarks like WL suite (also called SWIM), which analyzes the workload characteristics based on the number of jobs, arrival pattern and computation using synthesized representative data from real MapReduce traces. Other important arguments discussed in the paper are the accuracy of the reported benchmark metrics and the target platforms that it can evaluate. V. Zicari et al.

SaC-FRAPP: a scalable and cost-effective framework for privacy preservation over big data on cloud. Concurr. Comput. Pract. Exp. 25, 2561–2576 (2013). 3083 47. : The big data processing algorithm for water environment monitoring of the three Gorges reservoir area. Abstr. Appl. Anal. 1–7 (2014) 16 A. Emrouznejad and M. Marra Author Biographies Ali Emrouznejad is a Professor and Chair in Business Analytics at Aston Business School, UK. His areas of research interest include performance measurement and management, efﬁciency and productivity analysis as well as data mining.

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