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Wednesday, March 2 • 4:50pm - 5:10pm
Data Analytics Approaches & Tools: Scalable data-driven predictive model application for real-time operations monitoring

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Oil and Gas mission critical operations surveillance transitioned from merely real-time monitoring to embracing more proactive scheme. Increased leverage of data-driven models and machine learning techniques enabled early notification and lead time prediction of undesirable events.

Conventional model learning, verification, and testing process is inherently biased due to subjective sampling of population space or restrictive computation resources constraints. From a practical perspective, scaling models operability beyond sub-space of global operations footprint is a desired business objective that stretches the limits of training and maintaining validity of such models in a continuously evolving environment and growing big data feeds.

We present some of the lessons learned, challenges, and practical implications for designing scalable data-driven models and visualizations leveraging integrated big data streaming infrastructure in a distributed setting. The heterogeneous infrastructure support more active learning approach, adapting to the environment and process dynamics in a continuously evolving system.

Speakers
avatar for Mohamed Sidahmed

Mohamed Sidahmed

Data Analytics Scientist, BP


Wednesday March 2, 2016 4:50pm - 5:10pm
BioScience Research Collaborative Building (BRC), Room 103

Attendees (11)