title: Online Knowledge-Based Model for Big Data Topic Extraction.
abstract: 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.
authors.affiliation: Bahria University, Shangrilla Road, Sector E-8, Islamabad 44000, Pakistan; FAST-NUCES, Industrial Estate Road, Hayatabad, Peshawar 25000, Pakistan.
authors.forename: Muhammad Taimoor
authors.initials: MT
authors.lastname: Khan
authors.affiliation: COMSATS IIT, Kamra Road, Attock 43600, Pakistan.
authors.forename: Mehr
authors.initials: M
authors.lastname: Durrani
authors.affiliation: Bahria University, Shangrilla Road, Sector E-8, Islamabad 44000, Pakistan.
authors.forename: Shehzad
authors.initials: S
authors.lastname: Khalid
authors.affiliation: IMSciences, Phase 7, Hayatabad, Peshawar 25000, Pakistan.
authors.forename: Furqan
authors.initials: F
authors.lastname: Aziz
mesh_terms: D019992:Databases as Topic; D006801:Humans; D016247:Information Storage and Retrieval; D019359:Knowledge; D007858:Learning; D000069550:Machine Learning; D008962:Models, Theoretical; D009862:Online Systems