Applications for Big Data in of Intelligent Distributed Processing
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 6
Abstract
Abstract: Today, “Big Data” has posed new problem of over-information in many different areas. Such areasinclude health care (e.g., hospitals, bioinformatics), e-sciences (e.g., physics, chemistry, and geology), andsocial sciences (e.g., politics) [Bizer et al. 2011, Jung 2009]. Thus, as we have various types of hetrogeneousdata from a number of available sources, it is becoming increasingly more difficult to efficiently process suchBig Data. Distributed computing technologies (e.g., Hadoop, Hive and Pig) are strongly related to the “BigData” issues [Hogarth and Soyer 2015, Jung 2012]. Current big data issues are efficient distributed dataprocessing and management for example, information acquisition and stream processing, as well as dataintegration [Madden 2012]. Also, the big data involves heterogeneous information processing systemarchitectures in various application areas. These information processing systems need to exploit relevant solutions to support a number of intelligent services (e.g., knowledge management and decision making). The aim of this paper is to discuss state of art infrastructure and solutions in areas of distributed computing in different application areas involving big data. This will give an opportunity to push further the discussion upon the potential of knowledge and semantic systems across many communities. This paper will also discuss and analyse “Big data” sources and what is more important to identify the areas where Big data can be appliedand provide the knowledge that is not accessible for other types of analysis. Additionally, applications of Big data can be investigated either from static or dynamic perspective.
Authors and Affiliations
Dr Farheen Siddiqui
Keywords
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