Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Schedule: IoT sessions

11:50am12:30pm Wednesday, March 15, 2017
Sensors, IOT & Industrial Internet
Location: LL20 D Level: Advanced
Tim Gasper (Janrain)
Average rating: *****
(5.00, 1 rating)
Food production and preparation have always been labor and capital intensive, but with the internet of things, low-cost sensors, cloud-computing ubiquity, and big data analysis, farmers and chefs are being replaced with connected, big data robots—not just in the field but also in your kitchen. Tim Gasper explores the tech stack, data science techniques, and use cases driving this revolution. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Sensors, IOT & Industrial Internet
Location: LL20 D Level: Non-technical
Julie Lockner (17 Minds Corporation)
Average rating: *****
(5.00, 1 rating)
How can we empower individuals with special needs to reach their potential? Julie Lockner offers an overview of a project to develop collaboration applications that use wearable device data to improve the ability to develop the best possible care and education plans. Join in to learn how real-time IoT data analytics are making this possible. Read more.
4:20pm5:00pm Wednesday, March 15, 2017
Kishore R (GE)
Average rating: ***..
(3.00, 1 rating)
Kishore Reddipalli explores how to stream data at a large scale from the edge to the cloud to the client, detect anomalies, analyze machine data in stream and rest in an industrial world, and optimize the industrial operations by providing real-time insights and recommendations using big data technologies. Read more.
5:10pm5:50pm Wednesday, March 15, 2017
Real-time applications
Location: LL20 D Level: Intermediate
Michael Freedman (TimescaleDB)
Average rating: *****
(5.00, 3 ratings)
IoT applications often need more-complex queries than those supported by traditional time series databases. Michael Freedman outlines a new distributed time series database for such workloads, supporting efficient queries, including complex predicates across many metrics, while scaling out to support IoT ingest rates. Read more.
2:40pm3:20pm Thursday, March 16, 2017
Real-time applications
Location: 210 A/E Level: Advanced
Jeffrey Yau (Silicon Valley Data Science)
Average rating: ***..
(3.20, 5 ratings)
Thanks to frameworks such as Spark's GraphX and GraphFrames, graph-based techniques are increasingly applicable to anomaly, outlier, and event detection in time series. Jeffrey Yau offers an overview of applying graph-based techniques in fraud detection, IoT processing, and financial data and outlines the benefits of graphs relative to other techniques. Read more.