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December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference

Manufacturing conference sessions

1:45pm–2:25pm Thursday, 12/08/2016
One challenge when dealing with manufacturing sensor data analysis is to formulate an efficient model of the underlying physical system. Rajesh Sampathkumar shares his experience working with sensor data at scale to model a real-world manufacturing subsystem with simple techniques, such as moving average analysis, and advanced ones, like VAR, applied to the problem of predictive maintenance.
11:30am–12:00pm Tuesday, 12/06/2016
In semiconductor manufacturing, creating a high-yield process where sufficient portions of chips pass acceptance testing is extremely difficult to achieve. Data is collected and analyzed at every stage to improve yield and productivity. Amit Rustagi and Jingwen Ouyang share a Hadoop-based solution that reveals the true value and benefits of manufacturing data generated about every chip.
5:05pm–5:45pm Thursday, 12/08/2016
Rebecca Tien Yu Lin and Mon-Fong Mike Jiang offer an overview of a Hadoop-based big data solution helping the semiconductor industry increase yield by monitoring the huge amount of tool logs and the data generated from the FDC system.
4:15pm–4:55pm Wednesday, 12/07/2016
Picking up where his talk at Strata + Hadoop World in London left off, Gopal GopalKrishnan shares lessons learned from using components of the big data ecosystem for insights from industrial sensor and time series data and explores use cases in predictive maintenance, energy optimization, process efficiency, production cost reduction, and quality improvement.
11:15am–11:55am Wednesday, 12/07/2016
IHI has developed a common platform for remote monitoring and maintenance and has started leveraging Spark MLlib to get up speed developing applications for process improvement and product fault diagnosis. Yoshitaka Suzuki and Masaru Dobashi explain how IHI used PySpark and MLlib to improve its services and share best practices for application development and lessons for operating Spark on YARN.