A Scalable Machine Learning Online Service for Big Data Real-Time Analysis

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Ieee - The Institute Of Electrical And Electronics Engineers, Inc
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This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion; and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.
Proceedings of: IEEE Symposium Series on Computational Intelligence (SSCI 2014). Orlando, FL, USA, December 09-12, 2014.
Big data, Internet, Advertising data processing, Business data processing, Learning (artificial intelligence), Purchasing, Recommender systems
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2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD): proceedings (2014). IEEE, 1-8.