Analyzing data streams using a dynamic compact stream pattern algorithm

Oyewale, A ORCID: https://orcid.org/0000-0002-4468-6660, Hughes, CJ ORCID: https://orcid.org/0000-0002-4468-6660 and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2019, 'Analyzing data streams using a dynamic compact stream pattern algorithm' , International Journal Of Scientific And Technical Research In Engineering, 4 (2) , pp. 70-77.

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Abstract

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Data & Knowledge Engineering (DKE) has been known to stimulate the exchange of ideas and interaction between these two related fields of interest. DKE makes it possible to understand, apply and assess knowledge and skills required for the development and application data mining systems. With present technology, companies are able to collect vast amounts of data with relative ease. With no hesitation, many companies now have more data than they can handle. A vital portion of this data entails large unstructured data sets which amount up to 90 percent of an organization’s data. With data quantities growing steadily, the explosion of data is putting a strain on infrastructures as diverse companies having to increase their data center capacity with more servers and storages. This study conceptualized handling enormous data as a stream mining problem that applies to continuous data stream and proposes an ensemble of unsupervised learning methods for efficiently detecting anomalies in stream data.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Journal Of Scientific And Technical Research In Engineering
Publisher: IJSTRE
Related URLs:
Depositing User: Dr Chris Hughes
Date Deposited: 26 Apr 2019 08:40
Last Modified: 11 Oct 2019 10:41
URI: http://usir.salford.ac.uk/id/eprint/51172

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