big data algorithm

The course of Big Data Algorithms aims to teach students some basic algorithm design ideas on big data, including probabilistic algorithms, I/O efficient algorithms and parallel algorithms, so that students can be exposed to algorithm design and analysis that are different from traditional algorithm courses. ideas, and is guided by the latest research results, so that students participating in this course can understand the cutting-edge knowledge of big data algorithms. Through the study of this course, students can master the basic ideas of big data algorithm design, and through the homework of this course, master the technology of big data algorithm design and analysis.

• Chapter 1：Definition and characteristics of big data (15 minutes and 40 seconds)

• Chapter 2：Big Data Algorithms - Definition of Big Data Algorithms (6 minutes and 43 seconds)

• Chapter 3：Big Data Algorithm-Characteristics and Difficulties of Big Data Algorithm (3 minutes and 19 seconds)

• Chapter 4：Big data algorithm design and analysis (6 minutes and 33 seconds)

• Chapter 5：Definition of sublinear algorithm (6 minutes and 35 seconds)

• Chapter 6：Reservoir Sampling—Spatial Sublinear Algorithm (5 minutes and 38 seconds)

• Chapter 7：All-zero array judgment—time sublinear judgment algorithm (10 minutes and 19 seconds)

• Chapter 8：Frequent elements in data streams - basic knowledge (12 minutes and 12 seconds)

• Chapter 9：Frequent elements in data streams-algorithms and analysis (8 minutes and 51 seconds)

• Chapter 10：Spanning tree weight (1) (18 minutes and 36 seconds)

• Chapter 11：Spanning tree weight (2) (17 minutes and 32 seconds)

• Chapter 12：Array ordering judgment (9 minutes and 3 seconds)

• Chapter 13：External memory storage structure and external memory algorithm (14 minutes and 41 seconds)

• Chapter 14：External memory sorting algorithm (33 minutes and 46 seconds)

• Chapter 15：External memory sorting algorithm (2) (18 minutes and 56 seconds)

• Chapter 16：External memory search tree (7 minutes and 46 seconds)

• Chapter 17：B-Tree(1) (11 minutes and 46 seconds)

• Chapter 18：B-Tree(2) (13 minutes and 17 seconds)

• Chapter 19：KD tree (18 minutes and 24 seconds)

• Chapter 20：Table sorting and its applications (20 minutes and 33 seconds)

• Chapter 21：time forward processing (12 minutes and 42 seconds)

• Chapter 22：thumbnail method (11 minutes and 59 seconds)

• Chapter 23：MapReduce overview (13 minutes and 2 seconds)

• Chapter 24：Count (6 minutes and 4 seconds)

• Chapter 25：average calculation (5 minutes and 1 seconds)

• Chapter 26：Calculation of word contribution matrix (13 minutes and 17 seconds)

• Chapter 27：Join algorithm (24 minutes and 22 seconds)

• Chapter 28：Graph algorithm (1) (8 minutes and 27 seconds)

• Chapter 29：Graph algorithm (2) (12 minutes and 37 seconds)

• Chapter 30：Parallel algorithm based on iterative processing platform (20 minutes and 15 seconds)

• Chapter 31：Parallel algorithm based on graph processing platform (19 minutes and 5 seconds)

• Chapter 32：Examples of crowdsourcing (8 minutes and 46 seconds)

• Chapter 33：Elements of crowdsourcing (17 minutes and 25 seconds)

• Chapter 34：Example analysis of crowdsourcing algorithm (18 minutes and 47 seconds)

• Chapter 35：Definition of crowdsourcing (3 minutes and 53 seconds)