1 The Era of Big Data 2 Big Data Characteristics 3 A Big Data Processing Framework 4 Streaming Data and Streaming Features Outline 5 Concluding Remarks

Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary

 Motivation – Why do we need big data integration? – How has “small” data integration been done? – Challenges in big data integration  Schema alignment  Record linkage  Data fusion  Emerging topics

Data Objects and Attribute Types Basic Statistical Descriptions of Data Data Visualization Measuring Data Similarity and Dissimilarity 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

 What is data stream?  What is Concept Drift?  Data stream classification  Data stream clustering

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing 3 Supervised Hashing with Latent Factor Model 4 Supervised Multimodal Hashing with SCM 5 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 6 Conclusion 7 Reference

1 Introduction Problem Definition Existing Methods 2 Scalable Graph Hashing with Feature Transformation Motivation Model and Learning Experiment 3 Conclusion 4 Reference

电子科技大学：《大数据分析与挖掘 Big Data Analysis and Mining》课程教学资源（课件讲稿）Lecture 4 Sampling for Big Data

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference