Titlebook: Sublinear Algorithms for Big Data Applications; Dan Wang,Zhu Han Book 2015 The Author(s) 2015 Big data applications.Big data processing.Sm

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Application on a Smart Grid,ed. An illustration of a smart grid system is given in Fig. 5.1. With these powerful characteristics brought about by modern electrical technologies, a smart grid significantly alters the way utility companies, governments, customers, and business participants view electricity transmission and its associated services.

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Book 2015theoretical 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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2191-5768 dging 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.978-3-319-20447-5978-3-319-20448-2Series ISSN 2191-5768 Series E-ISSN 2191-5776

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2191-5768 inear 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, bri

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Basics for Sublinear Algorithms,pproximate factor in approximation algorithms. This confidence parameter is the key trade-off where the complexity of the algorithm can reduce to sublinear. We will rigidly define these parameters in this chapter.

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Basics for Sublinear Algorithms,the history of the development of sublinear algorithms in the theoretical research line. Intrinsically, sublinear algorithms can be considered as one branch of approximation algorithms with confidence guarantees. A sublinear algorithm says that the accuracy of the algorithm output will not deviate f