Sublinear Algorithms for Big Data Applications / by Dan Wang, Zhu Han.

By:

Wang, Dan [author.]

Contributor(s):

Material type: Text

TextLanguage: İngilizce Series: SpringerBriefs in Computer SciencePublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: 1 online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319204482

Subject(s):

LOC classification:

QA76.9.D3

Online resources:

Springer eBooks Online access link to the resource

Contents:

Introduction -- Basics for Sublinear Algorithms -- Applications for Wireless Sensor Networks -- Applications for Big Data Processing -- Applications for a Smart Grid -- Concluding Remarks.

Summary: The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

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Item type	Current library	Home library	Collection	Call number	Status	Notes	Date due	Barcode
E-Book	Merkez Kütüphane	Merkez Kütüphane	E-Kitap Koleksiyonu	QA76.9.D3EBK (Browse shelf(Opens below))	Geçerli değil-e-Kitap / Not applicable-e-Book			EBK00908

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Introduction -- Basics for Sublinear Algorithms -- Applications for Wireless Sensor Networks -- Applications for Big Data Processing -- Applications for a Smart Grid -- Concluding Remarks.

The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

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