Analyzing data streams using a dynamic compact stream pattern algorithm

A growing number of applications that generate massive streams of data need intelligent dataprocessing and online analysis. Data & Knowledge Engineering (DKE) has been known to stimulate theexchange of ideas and interaction between these two related fields of interest. DKE makes it possible tounderstand, apply and assess knowledge and skills required for the development and application data miningsystems. With present technology, companies are able to collect vast amounts of data with relative ease. With nohesitation, many companies now have more data than they can handle. A vital portion of this data entails largeunstructured data sets which amount up to 90 percent of an organization’s data. With data quantities growingsteadily, the explosion of data is putting a strain on infrastructures as diverse companies having to increase theirdata center capacity with more servers and storages. This study conceptualized handling enormous data as astream mining problem that applies to continuous data stream and proposes an ensemble of unsupervisedlearning methods for efficiently detecting anomalies in stream data