Algorithms for big data Moran Feldman.

Contents
Part I: Data Stream Algorithms
Chapter 1. Introduction to Data Stream Algorithms
Chapter 2. Basic Probability and Tail Bounds
Chapter 3. Estimation Algorithms
Chapter 4. Reservoir Sampling
Chapter 5. Pairwise Independent Hashing
Chapter 6. Counting Distinct Tokens
Chapter 7. Sketches
Chapter 8. Graph Data Stream Algorithms
Chapter 9. The Sliding Window Model
Part II: Sublinear Time Algorithms
Chapter 10. Introduction to Sublinear Time Algorithms
Chapter 11. Property Testing
Chapter 12. Algorithms for Bounded Degree Graphs
Chapter 13. An Algorithm for Dense Graphs
Chapter 14. Algorithms for Boolean Functions
Part III: Map-Reduce
Chapter 15. Introduction to Map-Reduce
Chapter 16. Algorithms for Lists
Chapter 17. Graph Algorithms
Chapter 18. Locality-Sensitive Hashing
Index

"This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms. To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data algorithms, and yet do not require tedious calculations or a very deep mathematical background"--