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

 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

§ 避免绝对值小的数作除数 § 避免两个相近的数据相减 § 要防止大数“吃掉”小数 § 尽量减少计算工作量 § 选用数值稳定性好的算法

一、循环码译码的原理 二、循环码的译码电路------梅吉特译码法（只纠一个错） 三、扩展汉明码的译码 四、缩短循环码的译码 五、捕错译码——原理 六、大数逻辑译码

【人工智能】面向调线调坡的点云大数据分析及深度模型研究

【机器学习】大数据背景下高校招生策略预测

【综述】大数据情报分析发展机遇及其挑战

• 4.1 概述 • 4.2 HBase访问接口 • 4.3 HBase数据模型 • 4.4 HBase的实现原理 • 4.5 HBase运行机制 • 4.6 HBase应用方案 • 4.7 HBase编程实践

Parallel DBMS technologies Proposed in the late eighties Matured over the last two decades Multi-billion dollar industry: Proprietary DBMS Engines intended as Data Warehousing solutions for very large enterprises Hadoop Spark UC Berkeley