Online Knowledge-Based Model for Big Data Topic Extraction

Online Knowledge-Based Model for Big Data Topic Extraction

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 st...

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Main Authors: Muhammad Taimoor Khan, Mehr Durrani, Shehzad Khalid, Furqan Aziz
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2016/6081804
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spelling doaj-84edb4a52abf42668dec23af2e76dcc92020-11-24T20:54:18ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732016-01-01201610.1155/2016/60818046081804Online Knowledge-Based Model for Big Data Topic ExtractionMuhammad Taimoor Khan0Mehr Durrani1Shehzad Khalid2Furqan Aziz3Bahria University, Shangrilla Road, Sector E-8, Islamabad 44000, PakistanCOMSATS IIT, Kamra Road, Attock 43600, PakistanBahria University, Shangrilla Road, Sector E-8, Islamabad 44000, PakistanIMSciences, Phase 7, Hayatabad, Peshawar 25000, PakistanLifelong 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.http://dx.doi.org/10.1155/2016/6081804
collection DOAJ
language English
format Article
sources DOAJ
author Muhammad Taimoor Khan
Mehr Durrani
Shehzad Khalid
Furqan Aziz
spellingShingle Muhammad Taimoor Khan
Mehr Durrani
Shehzad Khalid
Furqan Aziz
Online Knowledge-Based Model for Big Data Topic Extraction
Computational Intelligence and Neuroscience
author_facet Muhammad Taimoor Khan
Mehr Durrani
Shehzad Khalid
Furqan Aziz
author_sort Muhammad Taimoor Khan
title Online Knowledge-Based Model for Big Data Topic Extraction
title_short Online Knowledge-Based Model for Big Data Topic Extraction
title_full Online Knowledge-Based Model for Big Data Topic Extraction
title_fullStr Online Knowledge-Based Model for Big Data Topic Extraction
title_full_unstemmed Online Knowledge-Based Model for Big Data Topic Extraction
title_sort online knowledge-based model for big data topic extraction
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2016-01-01
description 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.
url http://dx.doi.org/10.1155/2016/6081804
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AT mehrdurrani onlineknowledgebasedmodelforbigdatatopicextraction
AT shehzadkhalid onlineknowledgebasedmodelforbigdatatopicextraction
AT furqanaziz onlineknowledgebasedmodelforbigdatatopicextraction
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