There is now more data being created than ever before and this data can be any
form of data, textual, multimedia, spatial etc. To process this data, several big data
processing platforms have been developed including Hadoop, based on the MapReduce
model and LexisNexis’ HPCC systems.
In this thesis we evaluate the HPCC Systems framework with a special interest in
multimedia data analysis and propose a framework for multimedia data processing.
It is important to note that multimedia data encompasses a wide variety of data including
but not limited to image data, video data, audio data and even textual data. While
developing a unified framework for such wide variety of data, we have to consider
computational complexity in dealing with the data. Preliminary results show that HPCC
can potentially reduce the computational complexity significantly. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_34534 |
| Contributors | Chinta, Vishnu (author), Kalva, Hari (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 42 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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