By Ali Emrouznejad

ISBN-10: 3319302639

ISBN-13: 9783319302638

ISBN-10: 3319302655

ISBN-13: 9783319302652

The major aim of this ebook is to supply the mandatory heritage to paintings with monstrous info by means of introducing a few novel optimization algorithms and codes able to operating within the huge facts environment in addition to introducing a few purposes in colossal facts optimization for either lecturers and practitioners , and to profit society, undefined, academia, and govt. providing functions in various industries, this publication may be necessary for the researchers aiming to analyses huge scale facts. numerous optimization algorithms for large facts together with convergent parallel algorithms, restricted reminiscence package set of rules, diagonal package deal strategy, convergent parallel algorithms, community analytics, and lots 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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The MapReduce framework allows for parallel processing of the data in HDFS. The processing of the data is broken down into the map and the reduce phases, which in turn allows parallelization. In the map phase the input data is distributed over the map processes, which are also called tasks. A single map task can process its part of the data independently of the other. The purpose of the reduce tasks is to combine the results from all the map tasks and calculate the overall result [46]. In Hadoop it is possible to store and process petabytes of unstructured data in a batch mode.

3 In-Memory Databases The extreme performance potentials of in-memory database management system technologies are very attractive to organizations when it comes to real-time or near real-time processing of large amounts of data. In a report by Gartner [38], in-memory infrastructures are defined as follows: “In-memory-enabling application infrastructure technologies consist of in-memory database management systems, in-memory data grids, high-performance messaging infrastructures, complex-event processing platforms, in-memory analytics and in-memory application servers.

Perspect. 28, 3–28 (2014). 3 39. : Predicting the behaviour of techno-social systems. Science 325(5939), 425–428 (2009). 1171990 40. : A new methodology for constructing a publication-level classification system of science. J. Am. Soc. Inf. Sci. Technol. 63, 2378–2392 (2012). doi:10. 22748 41. : Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft. Comput. 15, 2127–2140 (2010). 1007/s00500-010-0642-7 42. : A time efficient approach for detecting errors in big sensor data on cloud.

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Big Data Optimization: Recent Developments and Challenges by Ali Emrouznejad

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