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Sublinear Algorithms for Big Data Applications.

By: Wang, Dan.
Contributor(s): Han, Zhu.
Material type: TextTextSeries: eBooks on Demand.SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing, 2015Description: 1 online resource (94 p.).ISBN: 9783319204482.Subject(s): Big data | Computer algorithmsGenre/Form: Electronic books.Additional physical formats: Print version:: Sublinear Algorithms for Big Data ApplicationsDDC classification: 004 LOC classification: QA75.5-76.95Online resources: Click here to view this ebook.
Contents:
Preface; Contents; 1 Introduction ; 1.1 Big Data: The New Frontier; 1.2 Sublinear Algorithms; 1.3 Book Organization; References; 2 Basics for Sublinear Algorithms; 2.1 Introduction; 2.2 Foundations; 2.2.1 Approximation and Randomization; 2.2.2 Inequalities and Bounds; 2.2.3 Classification of Sublinear Algorithms; 2.3 Examples; 2.3.1 Estimating the User Percentage: The Very First Example; 2.3.2 Finding Distinct Elements; 2.3.2.1 The Initial Algorithm; 2.3.2.2 Median Trick in Boosting Confidence; 2.3.3 Two-Cat Problem; 2.4 Summary and Discussions; References
3 Application on Wireless Sensor Networks 3.1 Introduction; 3.1.1 Background and Related Work; 3.1.2 Chapter Outline; 3.2 System Architecture; 3.2.1 Preliminaries; 3.2.2 Network Construction; 3.2.3 Specifying the Structure of the Layers; 3.2.4 Data Collection and Aggregation; 3.3 Evaluation of the Accuracy and the Number of Sensors Queried; 3.3.1 MAX and MIN Queries; 3.3.2 QUANTILE Queries; 3.3.3 AVERAGE and SUM Queries; 3.3.3.1 The Initial Algorithm; 3.3.3.2 Utilizing Statistical Information About the Behavior of Data; 3.3.4 Effect of the Promotion Probability p; 3.4 Energy Consumption
3.4.1 Overall Lifetime of the System3.5 Evaluation Results; 3.5.1 System Settings; 3.5.2 Layers vs. Accuracy; 3.5.2.1 QUANTILE Queries; 3.5.2.2 AVERAGE Queries; 3.6 Practical Variations of the Architecture; 3.7 Summary and Discussions; References; 4 Application on Big Data Processing ; 4.1 Introduction; 4.1.1 Big Data Processing; 4.1.2 Overview of MapReduce; 4.1.3 The Data Skew Problem; 4.1.4 Chapter Outline; 4.2 Server Load Balancing: Analysis and Problem Formulation; 4.2.1 Background and Motivation; 4.2.2 Problem Formulation; 4.2.3 Input Models; 4.3 A 2-Competitive Fully Online Algorithm
4.4 A Sampling-Based Semi-online Algorithm4.4.1 Sample Size; 4.4.2 Heavy Keys; 4.4.3 A Sample-Based Algorithm; 4.5 Performance Evaluation; 4.5.1 Simulation Setup; 4.5.2 Results on Synthetic Data; 4.5.3 Results on Real Data; 4.6 Summary and Discussions; References; 5 Application on a Smart Grid ; 5.1 Introduction; 5.1.1 Background and Related Work; 5.1.2 Chapter Outline; 5.2 Smart Meter Data Analysis; 5.2.1 Incomplete Data Problem; 5.2.2 User Usage Behavior; 5.3 Load Profile Classification; 5.3.1 Sublinear Algorithm on Testing Two Distributions; 5.3.2 Sublinear Algorithm for Classifying Users
5.4 Differentiated Services5.5 Performance Evaluation; 5.6 Summary and Discussions; References; 6 Concluding Remarks ; 6.1 Summary of the Book; 6.2 Opportunities and Challenges
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
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QA75.5-76.95 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=3567781 Available EBL3567781

Preface; Contents; 1 Introduction ; 1.1 Big Data: The New Frontier; 1.2 Sublinear Algorithms; 1.3 Book Organization; References; 2 Basics for Sublinear Algorithms; 2.1 Introduction; 2.2 Foundations; 2.2.1 Approximation and Randomization; 2.2.2 Inequalities and Bounds; 2.2.3 Classification of Sublinear Algorithms; 2.3 Examples; 2.3.1 Estimating the User Percentage: The Very First Example; 2.3.2 Finding Distinct Elements; 2.3.2.1 The Initial Algorithm; 2.3.2.2 Median Trick in Boosting Confidence; 2.3.3 Two-Cat Problem; 2.4 Summary and Discussions; References

3 Application on Wireless Sensor Networks 3.1 Introduction; 3.1.1 Background and Related Work; 3.1.2 Chapter Outline; 3.2 System Architecture; 3.2.1 Preliminaries; 3.2.2 Network Construction; 3.2.3 Specifying the Structure of the Layers; 3.2.4 Data Collection and Aggregation; 3.3 Evaluation of the Accuracy and the Number of Sensors Queried; 3.3.1 MAX and MIN Queries; 3.3.2 QUANTILE Queries; 3.3.3 AVERAGE and SUM Queries; 3.3.3.1 The Initial Algorithm; 3.3.3.2 Utilizing Statistical Information About the Behavior of Data; 3.3.4 Effect of the Promotion Probability p; 3.4 Energy Consumption

3.4.1 Overall Lifetime of the System3.5 Evaluation Results; 3.5.1 System Settings; 3.5.2 Layers vs. Accuracy; 3.5.2.1 QUANTILE Queries; 3.5.2.2 AVERAGE Queries; 3.6 Practical Variations of the Architecture; 3.7 Summary and Discussions; References; 4 Application on Big Data Processing ; 4.1 Introduction; 4.1.1 Big Data Processing; 4.1.2 Overview of MapReduce; 4.1.3 The Data Skew Problem; 4.1.4 Chapter Outline; 4.2 Server Load Balancing: Analysis and Problem Formulation; 4.2.1 Background and Motivation; 4.2.2 Problem Formulation; 4.2.3 Input Models; 4.3 A 2-Competitive Fully Online Algorithm

4.4 A Sampling-Based Semi-online Algorithm4.4.1 Sample Size; 4.4.2 Heavy Keys; 4.4.3 A Sample-Based Algorithm; 4.5 Performance Evaluation; 4.5.1 Simulation Setup; 4.5.2 Results on Synthetic Data; 4.5.3 Results on Real Data; 4.6 Summary and Discussions; References; 5 Application on a Smart Grid ; 5.1 Introduction; 5.1.1 Background and Related Work; 5.1.2 Chapter Outline; 5.2 Smart Meter Data Analysis; 5.2.1 Incomplete Data Problem; 5.2.2 User Usage Behavior; 5.3 Load Profile Classification; 5.3.1 Sublinear Algorithm on Testing Two Distributions; 5.3.2 Sublinear Algorithm for Classifying Users

5.4 Differentiated Services5.5 Performance Evaluation; 5.6 Summary and Discussions; References; 6 Concluding Remarks ; 6.1 Summary of the Book; 6.2 Opportunities and Challenges

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