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Recorded at SpringOne2GX 2015. Presenters: Fred Melo & William Markito Oliveira Big Data Track Slides: http://www.slideshare.net/SpringCentral/implementing-a-highly-scalable-stock-prediction-system-with-r-apache-geode-and-spring-xd
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.