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


















Evolving Knowledge Ecosystems for Big Data Understanding 11 a sample of an appropriate size for being effective may bring bias, harm correctness, and accuracy. Otherwise, analyzing Big Data in source volumes will definitely distort timeliness. • Therefore, Big Data is not always the best option. A question that requires research effort in this context is about the appropriate sample, size, and granularity to best answer the question of a data analyst. • Consequently, taken off-context Big

problem could be solved using multiissue negotiations on semantic contexts, for example, following the approach of Ermolayev et al. (2005) and Davidovsky et al. (2012). For assuring the consistency in the updated ontology modules after alignment, several approaches are applicable: incremental updates for atomic decompositions of ontology modules (Klinov et al. 2012); checking correctness of ontology contexts using ontology design patterns approach (Gangemi and Presutti 2009); evaluating formal

evolution, a KO and its genome are resistent to mutagenic factors and do not change at once because of any incoming influence, but only because of those which could not be ignored because of their strength. Different genome elements may be differently resistant. Let us illustrate different aspects of mutation and resistance using our Boeing example. As depicted in Figure 1.9, the change of the AirPlaneMaker concept name (to PlaneMaker) in the genome did not happen though a new assertion had been

Navigation 77 Another important feature is the ability to get advanced faceted results from queries on the large volumes of available data: this type of queries allows the user to access the information in the store, along multiple explicit dimensions, and after the application of multiple filters. This interaction paradigm is used in mining applications and allows to analyze and browse data across multiple dimensions; the faceted queries are especially useful in e-commerce websites

scalability. The main reasons for using a database of this type include the presence of complex relationships that suggest tree structures or graphs, and the presence of complex data, that is, when there are components of variable length and in particular multi-dimensional arrays. Other reasons are related to the presence of a database that must be geographically distributed, and which is accessed via a processor grid, or the use of more than one language or platform, and the use of workplace

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