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

1 Introduction Problem Definition Existing Methods Motivation and Contribution 2 Isotropic Hashing Model Learning Experimental Results 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction 2 AsySVRG 3 SCOPE 4 Conclusion

Proquest Infinite Information. Innovations ProQuest Information Learning

《并行与分布式程序设计》课程教学参考书：分布式与云计算《Spark大数据处理技术、应用与性能优化》（PDF扫描版）

 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

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 7 Hadoop-Spark

Key Node Identification Community Detection

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

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