Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

iot conference sessions

1:15pm–1:55pm Wednesday, 09/30/2015
Roy Ben-Alta (Amazon Web Services)
Amazon Kinesis is a fully managed service for real-time streaming big data ingestion and processing. This talk explores Kinesis concepts in detail, including best practices for scaling your core streaming data ingestion pipeline. We then discuss building and deploying Kinesis processing applications using capabilities like Kinesis Client Libraries, AWS Lambda, and Amazon EMR (via Spark).
5:25pm–6:05pm Wednesday, 09/30/2015
Ian Eslick (VitalLabs)
Capturing and integrating device-based and other health data for research is frustratingly difficult. We explain the open source technology frame​work for capturing and routing device-based health data for use by healthcare providers and for access, via a trusted analytic container, to ​​researchers we developed, working with O’Reilly Media and support from the Robert Wood Johnson Foundation.​
1:15pm–1:55pm Wednesday, 09/30/2015
Sarah Aerni (Pivotal)
The promise of IoT is that it will forever change the way people and businesses interact with the world. Using illustrative use cases, Pivotal will demonstrate the fundamental concepts required to drive true impact from these connected devices. We will cover which models are most appropriate, what considerations around data access and processing are critical, and which tools available.
11:20am–12:00pm Thursday, 10/01/2015
Michael Hausenblas (Mesosphere)
By 2020, researchers estimate there will be 100 million internet connected devices. To process this data in real time—whether from mobile phones or jet engines—will be the new normal. How are companies today adapting to this new real time stream of data?
2:05pm–2:45pm Thursday, 10/01/2015
Ankur Gupta (Bitwise Inc.)
Using an open source technology stack, we implemented a solution for real-time analysis of sensor data from mining equipment. We will share the technical architecture used to show the tools we implemented for real-time complex event processing, why we implemented Spark instead of Storm, some of the challenges faced, benchmarks achieved, and tips for easy integration.
1:30pm–2:00pm Tuesday, 09/29/2015
Tanzeem Choudhury (Cornell and HealthRhythms)
How ubiquitous computing is transforming the treatment of mental health disorders
4:35pm–5:15pm Wednesday, 09/30/2015
Hari Shreedharan (Cloudera), Anand Iyer (Cloudera)
Over the past year, Spark Streaming has emerged as the leading platform to implement IoT and similar real-time use cases. This session includes a brief introduction to Spark Streaming’s micro-batch architecture for real-time stream processing, as well as a live demo of an example use case that includes processing and alerting on-time series data (such as sensor data).
11:20am–12:00pm Thursday, 10/01/2015
Haden Land (Lockheed Martin IS&GS), Jason Loveland (Lockheed Martin)
Slides:   1-PPTX 
Lockheed Martin builds unmanned and manned human space systems, which require systems that are tested for all possible conditions – even for unforeseen situations. We present a test system that is a learning system built on big data technologies, that supports the testing of the Orion Multi-Purpose Crew Vehicle being designed for long-duration, human-rated deep space exploration.
2:55pm–3:35pm Wednesday, 09/30/2015
Håkan Jonsson (Sony Mobile Communications)
In this talk we will show how Sony Mobile uses large scale analytics on Spark to generate insights to Lifelog users about themselves and the population, and how we use analytics to build a user lifecycle model that allows us to take actions toward increased user engagement and retention.
1:15pm–1:55pm Thursday, 10/01/2015
Yan Zhang (Microsoft)
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification), and showcases how the models can be conveniently trained and compared with different algorithms.
4:35pm–5:15pm Thursday, 10/01/2015
Susanna Pirttikangas (University of Oulu)
Oulu Smart City has a lively living lab tradition; we continuously collect data and expand our ecosystem of companies, research institutes, city officials, and citizens, and develop data-intensive services on top of the ecosystem. We present real use cases implementing big data platforms and development of higher level distributed reasoning and machine learning to exploit our data lake.