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2017 | OriginalPaper | Buchkapitel

Advanced High Performance Computing for Big Data Local Visual Meaning

verfasst von : Ozgur Aksu

Erschienen in: Big Data and Visual Analytics

Verlag: Springer International Publishing

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Abstract

Being able to scale interactive analysis for big data clusters is gaining more importance with each passing day in our present time. For example, according to the Behaviors Questionnaire performed in 2015 by K.D. Nuggets, around one fourth of 459 participants tried to interpret data clusters that exceed 1 terabyte and 100 petabytes. One of the subjects of previous studies is the Canonic Method, which is used to form the meanings of big data in a fast and efficient manner, because approximate responses given as based on sampling usually bring benefits as much as the response itself; and the sampling may also lessen the burden of cognitive confusion in meaning. As a result of previous studies conducted on database environments, extremely precious data have been obtained in terms of sampling and local visual inference; however, in the present study, firstly, the new methods and the system problems on the access to inference data have been focused on [1–4].

Today, data production is developing at an amazing speed. In our present day, the exponential technical developments, analogue sensor data, adaptive digital systems, scientific high-sensitivity sensors, smart devices and integral-theoretical models cause that data are produced at an extremely great speed. It is expected that global data volume will grow at a speed of 40-fold each year and reach 44 zettabytes by 2020 [5]. The term “big data” has been produced in order to cope with the volume, speed and variety of the data produced, and to make sense of this data trend that is developing day by day. Big data are becoming the new focal point of technology in many fields. A series of additional tools and mechanisms may be integrated to big data systems in order to obtain, store and process different data. These systems use the advantage of a tremendous parallel processing power for the purpose of performing complex conversions and analyses. On the other hand, designing and using a big data system intended for a certain application is not practical [6–7], because data come from more than one source that are heterogeneous and autonomous, and are in complex and changing relations with each other growing in an adaptive manner. In addition to these, the rise of big data applications in which data collection phenomenon is increasing at an amazing speed is beyond the capacity of today’s hardware and software platforms in terms of managing, storing and processing data within a reasonable time [6].

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Titel: Advanced High Performance Computing for Big Data Local Visual Meaning
verfasst von: Ozgur Aksu
Verlag: Springer International Publishing
Buch: Big Data and Visual Analytics
Print ISBN: 978-3-319-63915-4

Electronic ISBN: 978-3-319-63917-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63917-8_8

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