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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 6081804, 10 pages
Research Article

Online Knowledge-Based Model for Big Data Topic Extraction

1Bahria University, Shangrilla Road, Sector E-8, Islamabad 44000, Pakistan
2FAST-NUCES, Industrial Estate Road, Hayatabad, Peshawar 25000, Pakistan
3COMSATS IIT, Kamra Road, Attock 43600, Pakistan
4IMSciences, Phase 7, Hayatabad, Peshawar 25000, Pakistan

Received 4 February 2016; Revised 16 March 2016; Accepted 24 March 2016

Academic Editor: Leo Chen

Copyright © 2016 Muhammad Taimoor Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.