Building Systems for Big Data Analytics: From SQL to Machine Learning and Graph Analysis

author: Yuanyuan Tian, IBM Almaden Research Center, IBM Research
published: Dec. 1, 2017,   recorded: August 2017,   views: 772

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Big data is being generated everywhere at all times. It has become more and more important to analyze the large volume of data to drive actionable insights. There are different types of analytics that can exploit the wealth of information in big data, from traditional business analytics using SQL to complex machine learning and graph analysis. In this keynote, Dr. Tian will share her experience and lessons learned in building different types of analytics systems for big data. In particular, this talk will feature her work on studying distributed join algorithms for SQL-on-Hadoop systems, building the large-scale machine learning system SystemML, and inventing new processing models for distributed graph processing.

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