Download e-book for kindle: Big Data Optimization: Recent Developments and Challenges by Ali Emrouznejad

By Ali Emrouznejad

ISBN-10: 3319302639

ISBN-13: 9783319302638

ISBN-10: 3319302655

ISBN-13: 9783319302652

The major goal of this e-book is to supply the required historical past to paintings with huge information via introducing a few novel optimization algorithms and codes able to operating within the immense info surroundings in addition to introducing a few purposes in giant information optimization for either teachers and practitioners , and to learn society, undefined, academia, and govt. providing purposes in various industries, this booklet should be worthwhile for the researchers aiming to analyses huge scale info. numerous optimization algorithms for large info together with convergent parallel algorithms, restricted reminiscence package set of rules, diagonal package deal technique, convergent parallel algorithms, community analytics, and plenty of extra were explored during this book.

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Extra resources for Big Data Optimization: Recent Developments and Challenges

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V. Zicari et al. OLTP and OLAP systems. Just recently the TPC have formed a new group for the standardization of a big data benchmark [65] along with other similar initiatives like the Big Data Top 100 [66] and the Big Data Benchmarking Community [67]. However, the existing and emerging big data applications and platforms have very different characteristics (“3Vs”) compared to the traditional transactional and analytical systems. These new platforms can store various types of data (structured, unstructured or semi-structured) with the schema (schema-on-read) defined just before accessing the data.

We predict key metrics for the different businesses—everything from television ratings, to how an audience will respond to marketing campaigns, to the value of a particular opening weekend for the box office. To do this, we use machine learning regression and classification algorithms, semantic analysis, monte-carlo methods, and simulations. For instance, our cinema distribution company operates in dozens of countries. 
Each country might have dozens more cinema operators, all sending data in different formats and at different qualities.

1121–1127. IEEE (2013). 163 44. : A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems. Int. J. Approx. Reason. 55, 896–907 (2014). 003 45. : A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud. J. Comput. Syst. Sci. 80, 1008–1020 (2014). 007 46. : SaC-FRAPP: a scalable and cost-effective framework for privacy preservation over big data on cloud. Concurr. Comput. Pract. Exp. 25, 2561–2576 (2013).

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