Laboratory for Interactive Optimization and Learning

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Big Data

Analytics

Data, Predictions, and Machine Learning

Big Data Analytics is concerned with generating insights from large amounts of operational data, often hundreds of gigabyte in size, which then form the data for decision-making processes. Only very recently, Big Data Analytics became a reality with the abundance of low-cost high-performance sensors and new computing paradigms such as e.g., GPU computing.

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Right: Performance evaluation of various failure mode predictors

Left: Activation-supervised training of a Deep Neural Network

Big Data Analytics and Deep Learning.


Many real-world analytics applications rely on machine learning and signal processing on very large amounts of data. The basic idea is that a representative model is directly derived from the data rather some abstract hypothesis. The underlying data can be real-time data collected from sensors (e.g., 3D cameras or vibration sensors), video signals, or very large natural language corpora (e.g., social media data).


We leverage GPU-computing, outperforming traditional setups by several orders of magnitude, to effectively train deep neural networks and perform signal processing.


Current involvements and projects.


Natural-Language Processing. Recurrent Neural Networks are a powerful tool to process natural language. Typical applications include sentiment analysis and entity tagging, and have been successfully used to predict user behavior from social-media data.  


Predictive Maintenance. Deep architectures are also a very powerful tool to capture machine/asset deterioration and predict failure modes and failure times. This data can then be leveraged in decision-making to enable proactive maintenance and streamline spare-parts logistics.  





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