000			02132cam a22002778i 4500
999			_c35234 _d35234
003			CUTN
005			20210706144059.0
008			200310s2020 nju 000 0 eng
020			_a9789811204739
020			_z9789811204746
020			_z9789811204753
041			_aEnglish
042			_apcc
082	0	0	_a005.701 _223 _bFEL
100	1		_aFeldman, Moran,
245	1	0	_aAlgorithms for big data _cMoran Feldman.
300			_apages cm
505			_tContents 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
520			_a"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"--
650		0	_aAlgorithms.
942			_2ddc _cBOOKS
100	1		_eauthor.
263			_a2007
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg