Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: IoT and its applications sessions

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11:1511:55 Wednesday, 1 May 2019
Mike Olson (Cloudera)
It's easier than ever to collect data -- but managing it securely, in compliance with regulations and legal constraints is harder. There are plenty of tools that promise to bring machine learning techniques to your data -- but choosing the right tools, and managing models and applications in compliance with regulation and law is quite difficult. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Jian Chang (Alibaba Group), Sanjian Chen (Alibaba Group)
We would like to share the architecture design and many detailed technology innovations of Alibaba TSDB, a state-of-the-art database for IoT data management, from years of development and continuous improvement. Read more.
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14:5515:35 Wednesday, 1 May 2019
Geir Endahl (Cognite), Daniel Bergqvist (Google)
Learn how Cognite is developing IIoT smart maintenance systems that can process 10M samples/second from thousands of sensors. We’ll review an architecture designed for high performance, robust streaming sensor data ingest and cost-effective storage of large volumes of time series data, best practices for aggregation and fast queries, and achieving high-performance with machine learning. Read more.
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12:0512:45 Thursday, 2 May 2019
Alasdair Allan (Babilim Light Industries)
A arrival of new generation of smart embedded hardware may cause the demise of large scale data harvesting. In its place smart devices will allow us process data at the edge, allowing us to extract insights from the data without storing potentially privacy and GDPR infringing data. The current age where privacy is no longer "a social norm" may not long survive the coming of the Internet of Things. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Christian Hidber (bSquare)
Reinforcement learning (RL) learns complex processes autonomously like walking, beating the world champion in go or flying a helicopter. No big data sets with the “right” answers are needed: the algorithms learn by experimenting. We show “how” and “why” RL works in an intuitive fashion & highlight how to apply it to an industrial, hydraulics application with 7000 clients in 42 countries. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Jane McConnell (Teradata), Sun Maria Lehmann (Equinor)
In Upstream Oil and Gas, a vast amount of the data requested for analytics projects is “scientific data” - physical measurements about the real world. Historically this data has been managed “library-style” in files - but to provide this data to analytics projects, we need to do something different. Sun and Jane discuss architectural best practices learned from their work with subsurface data. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Christopher Hooi (Land Transport Authority of Singapore)
The Fusion Analytics for Public Transport Event Response (FASTER) system provides a real-time advanced analytics solution for early warning of potential train incidents. Using novel fusion analytics of multiple data sources, FASTER harnesses the use of engineering and commuter-centric IoT data sources to activate contingency plans at the earliest possible time and reduce impact to commuters. Read more.
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16:3517:15 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
GRDF helps bring natural gas to nearly 11 million customers everyday. In partnership with GRDF, Dataiku worked to optimise the manual process of qualifying addresses to visit and ultimately save GRDF time and money. This solution was the culmination of a year-long adventure in the land of maintenance experts, legacy IT systems and agile development. Read more.