Big Data Computing

Big Data Computing

Language: English

Pages: 564

ISBN: 1466578378

Format: PDF / Kindle (mobi) / ePub


Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis.

Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches.

Comprised of five sections, the book focuses on:

  • What Big Data is and why it is important
  • Semantic technologies
  • Tools and methods
  • Business and economic perspectives
  • Big Data applications across industries

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layers that provide data. Toward Evolving Knowledge Ecosystems for Big Data Understanding 47 Ontologies are the “blood and flesh” of the KOs and the whole ecosystem as they are both the code registering a desired evolutionary change and the result of this evolution. From the data-processing viewpoint, the ontologies are consensual knowledge representations that facilitate improving data integration, transformation, and interoperability between the processing nodes in the infrastructure. A

M H H H H H H H M L L H H H M L H Hash index, HDFS, RDF-graph H L H Remote user Interfaces for interfaces, on-demand multiple access access, from different ad-hoc SQL query interfaces application MultiDistributed dimensional multi-level index, HDFS, tree indexing, index, HDFS, index, RDBMS like RDBMS like M H H HDFS-UI web API, common app, API, line common line interfaces, interfaces, concurrency AMS access M H L H M M Distributed index, RDBMS like H H M API

has been notified for the educational domain. For example, the student profile analysis for the purpose of the personalized courses are typically locally based and are accumulating a lower number of information with respect to global market analysis, security, social media, etc. The latter cases are typically deployed as multisite geographical distributed databases at the worldwide level, while educational applications are usually confined at regional and local levels. For instance, in high

successfully—there are no transactional guarantees. Current Hadoop distributions challenges • Getting data in and out of Hadoop. Some Hadoop distributions are limited by the append-only nature of the Hadoop Distributed File System (HDFS) that requires programs to batch load and unload data into a cluster. • The lack of reliability of current Hadoop software platforms is a major impediment for expansion. • Protecting data against application and user errors. • Hadoop has no backup and restore

(Berners-Lee 1998), a more compact and readable alternative. Turtle (Beckett and Berners-Lee 2008) inherits N3 compact * http://sindice.com/ Management of Big Semantic Data 147 ability adding interesting extra features, for example, abbreviated RDF data sets. RDF/JSON (Alexander 2008) has the advantage of being coded in a language easier to parse and more widely accepted in the programing world. Although all these formats present features to “abbreviate” constructions, they are still

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