Industrial IoT and Machine Learning Webinar

Industrial Iinternet of Things and Machine Learning

Making Wind Energy Cost Competitive, sponsored by the Independent Oracle Users Group
This is a case study of the Fluitec Wind system that M&S Director of Data Science, Bob Liekar, lead and will be presenting on Wednesday, May 13, 12:45pm–2:00pm EDT.

About The Webinar

The Fluitec Wind Tribo-Analytics system is a production application that utilizes multiple Big Data disciplines. This case study presents a real-world application of Big Data and IoT (Internet of Things). The techniques and algorithms can be applied to many other industries.

From Fluitec: “Wind turbines are expensive to operate. They are remote and distributed with highly varied environmental conditions and operational loads … they produce vast quantities of data … Fluitec Wind employs advanced data mining and predictive analytics to reduce the cost of operations and maintenance of wind turbines.”

Data wrangling is a major challenge. In addition to the high volume of machine-generated sensor data, Fluitec has to integrate complex operational and oil sample data that is dramatically different in frequency, timing, volume and structure.

Data quality is crucial. Predictive models are inherently sensitive to missing data and also to the transformations used to normalize and restructure data. Complex data pre-processing was necessary.

In this webinar you will learn how Fluitec solved these real-world challenges through;
• Data wrangling on a Big Data scale
• Predictive modeling and data mining of complex, heterogeneous data sources
• Utilization of cloud resources (AWS)
• Integrating Big Data tools such as Hadoop with MySQL and Java


If you missed the webinar, you are now able to access the recording on the IOUG website.

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