Screen LibrarySublinear algorithms for big data applications /
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 a...
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| Main Authors: | |
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| Group Author: | |
| Published: |
Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | [2015] |
| Literature type: | Book |
| Language: | English |
| Series: |
SpringerBriefs in computer science,
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| Subjects: | |
| 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, |
| Carrier Form: | xi, 85 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9783319204475 3319204475 |
| Index Number: | QA9 |
| CLC: |
TP311.131 TP301.6 |
| Call Number: | TP301.6/W246 |
| Contents: | Introduction -- Basics for Sublinear Algorithms -- Applications for Wireless Sensor Networks -- Applications for Big Data Processing -- Applications for a Smart Grid -- Concluding Remarks. |