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

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerBriefs in Computer Science | SpringerLink Bücher | Springer eBook Collection Computer SciencePublisher: Cham : Springer, 2015Description: Online-Ressource (XI, 85 p. 30 illus., 20 illus. in color, online resource)ISBN:
  • 9783319204482
Subject(s): Additional physical formats: 9783319204475 | Druckausg.: 978-331-92044-7-5 LOC classification:
  • QA76.9.D3
DOI: DOI: 10.1007/978-3-319-20448-2Online resources: 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 communitiesPPN: PPN: 1657509281Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS
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