Knowledge Discovery in Data Streams

Authored by: Xuan Hong Dang , Kok-Leong Ong

Encyclopedia of Library and Information Science, Fourth Edition

Print publication date:  November  2017
Online publication date:  November  2017

Print ISBN: 9781466552593
eBook ISBN: 9781315116143
Adobe ISBN:

10.1081/E-ELIS4-120043643

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Abstract

Knowing what to do with the massive amount of data collected has always been an ongoing issue for many organizations. While data mining has been touted to be the solution, it has failed to deliver the impact despite its successes in many areas. One reason is that data mining algorithms were not designed for the real world, i.e., they usually assume a static view of the data and a stable execution environment where resources are abundant. The reality however is that data are constantly changing and the execution environment is dynamic. Hence, it becomes difficult for data mining to truly deliver timely and relevant results. Recently, the processing of stream data has received many attention. What is interesting is that the methodology to design stream-based algorithms may well be the solution to the above problem. In this entry, we discuss this issue and present an overview of recent works.

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