Presented By O'Reilly and Cloudera
December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference

Iot conference sessions

9:30am–9:40am Wednesday, 12/07/2016
Elevating the intelligence of the whole ecosystem of things is imperative to ensure the proliferation of IoT devices doesn't result in disparate, detached machinery. Isaac Jacob explains how Fusionex intends to achieve this by leveraging robust, intuitive BDA solutions with real-time capabilities.
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.
1:45pm–2:25pm Wednesday, 12/07/2016
Embedding operational analytics with the IoT enables organizations to act on insights in real time. Devin Deen and Dnyanesh Prabhu walk you through examples from Sky TV and NZ Bus—two businesses that iteratively developed their analytic capabilities integrating the IoT on Hadoop, allowing people and process changes to keep pace with technical enablement.
11:15am–11:55am Thursday, 12/08/2016
The interconnected world presents unprecedented opportunities to gain new insights on behavior, both human and nonhuman alike. Likewise, it also poses unprecedented challenges on how organizations can act on these moments of opportunities in time. Michael O'Connell and San Zaw share real-world case studies demonstrating how real-time analytics solves these challenges.
2:35pm–3:15pm Thursday, 12/08/2016
Jennifer Marsman, Ranveer Chandra, and Wee Hyong Tok explore the various drone technologies that are currently available and explain how to acquire and analyze real-time signals from drones to design intelligent applications.
5:05pm–5:45pm Thursday, 12/08/2016
Data ethics covers more than just privacy. In a connected world where most people rely on data-driven services, opting out and locking data away is hardly an option. More important than keeping data private is ensuring fairness and preventing abuse. Joerg Blumtritt and Heather Dewey-Hagborg show how to deal with data in an ethical way that has sound economic value.
11:15am–11:55am Wednesday, 12/07/2016
In 2007, a computer game company decided to jump ahead of competitors by capturing and using data created during online gaming, but it wasn't prepared to deal with the data management and process challenges stemming from distributed devices creating data. Mark Madsen shares a case study that explores the oversights, failures, and lessons the company learned along its journey.
12:00pm–12:30pm Tuesday, 12/06/2016
Modern cars produce data. Lots of data. And Formula 1 cars produce more than their fair share. Ted Dunning presents a demo of how data streaming can be applied to the analytics problems posed by modern motorsports. Although he won't be bringing Formula 1 cars to the talk, Ted demonstrates a physics-based simulator to analyze realistic data from simulated cars.
5:05pm–5:45pm Wednesday, 12/07/2016
Creating big data solutions that can process data at terabyte scale and produce spatial-temporal real-time insights at speed demands a well-thought-through system architecture. Chandras Sekhar Saripaka details the production architecture at DataSpark that works through terabytes of spatial-temporal telco data each day in PaaS mode and showcases how DataSpark operates in SaaS mode.
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.
5:05pm–5:45pm Wednesday, 12/07/2016
Takayuki Nishikawa and Ei Yamaguhi explain how Panasonic developed an integrated data analytics platform to analyze the increasing number of home appliances logs from its IoT products, achieving scalability for millions of households and a 10x improvement in processing time with Hadoop and Hive, in the process gaining more reliable knowledge about users’ lifestyles with Spark MLlib.
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.
2:35pm–3:15pm Wednesday, 12/07/2016
Combining the power of the IoT and big data analytics opens the doors to a wide range of opportunities for organizations to solve new challenges that create an impact on the world that we live in. Frank Saeuberlich and Karthik Thirumalai explain why data management, data integration, and multigenre analytics are foundational to driving business value from IoT initiatives.
1:45pm–2:25pm Wednesday, 12/07/2016
Shao Wei Ying explains how mobility intelligence derived from telco big data informs us about the state of our urban infrastructure, economic activities, and public safety.