Analyzing data streams using a dynamic compact stream pattern algorithm

Oyewale, A; Hughes, CJ; Saraee, MH

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.

Citation

Oyewale, A., Hughes, C., & Saraee, M. (2019). Analyzing data streams using a dynamic compact stream pattern algorithm

• Journal Article Type: Article
• Online Publication Date: Apr 30, 2019
• Publication Date: Apr 30, 2019
• Deposit Date: Apr 26, 2019
• Publicly Available Date: May 30, 2019
• Journal: International Journal Of Scientific And Technical Research In Engineering
• Volume: 4
• Issue: 2
• Pages: 70-77
• Publisher URL: https://www.ijstre.com/v4i2.php
• Related Public URLs: https://www.ijstre.com/index.php