DATA1 园D+ GN国S DATA XTAL2 TAL1 四GL DATA ALE 10 19 EOT_N csN回

• Concepts and Terminology • Analog and Digital Data Transmission • Transmission Impairments • Channel Capacity

Procedural paradigm: Software is organized around the notion of procedures Procedural abstraction —Works as long as the data is simple Adding data abstractions —Groups together the pieces of data that describe some entity

Chapter 8 Objectives After you have read and studied this chapter, you should be able to Declare and manipulate data of the char data type. Write string processing programs using String and StringBuffer objects. Differentiate the String and StringBuffer classes and use the correct class in solving given task. Distinguish the primitive and reference data types and show how the memory allocation between the two is different

Purpose of speech corpora Factors to be considered in data creation Data creation Data transcription Learning from corpora Chinese Corpus Consortium (CCC)

1 Introduction 2 SAS Language 2.1 Proc Step and Data Step 2.2 SAS Logical Library 2.2.1 Access SAS file 2.2.2 View SAS library and file 3 SAS Programming 3.1 Reading data by DATA STEP 3.2 Output format 3.3 Manipulate datasets 3.3.1 SET statement 3.3.2 SORT proc 3.4 Logical statements 3.4.1 IF-THEN statement 3.4.2 SELECT-WHEN statement 3.4.3 DO-ENDS statement 3.4.4 DO-WHILE DO-UNTIL statement 3.5 OPERATIONS 4 Basic statistical analysis 4.1 Descriptive Statistics Proc 4.1.1 MEANS proc 4.1.2 SUMMARY proc 4.1.3 UNIVARIATE proc 4.1.4 TABULATE PROC 4.1.5 GCHART proc 4.1.6 GPLOT proc 4.2 INFERENTIAL Statistics 4.2.1 T-TEST 4.2.2 Chi-square tests 4.2.3 Correlation 4.2.4 Regression

15.1 INTRODUCTION 15.2 COMMON DATA-ANALYSIS METHODOLOGY 15.3 EXPLORING RELATIONSHIPS 1 15.4 WORKING WITH SAMPLE DATA 15.5 THE INITIAL DATA ANALYSIS AND MINITAB 15.6 SUMMARY

◼ Pattern Mining: A Road Map ◼ Pattern Mining in Multi-Level, Multi-Dimensional Space ◼ Constraint-Based Frequent Pattern Mining ◼ Mining High-Dimensional Data and Colossal Patterns ◼ Mining Compressed or Approximate Patterns ◼ Pattern Exploration and Application ◼ Summary

10.1 Introduction 10.2 Empirical Studies and Statistical Inference 10.3 Important Features of Big Data 10.4 Big Data Analysis and Statistics 10.5 Machine Learning and Statistics 10.6 Conclusion

 Data Center Introduction  Data Center Network Architectures  Fat-Tree  VL2  DCell  BCube  FiConn