Knowledge discovery in data streams

Dang, Xuan Hong and Ong, Kok-Leong 2010, Knowledge discovery in data streams, in Encyclopedia of library and information sciences, CRC Press, Boca Raton, Fla., pp.3115-3128.

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Title Knowledge discovery in data streams
Author(s) Dang, Xuan Hong
Ong, Kok-Leong
Title of book Encyclopedia of library and information sciences
Editor(s) Bates, Marcia J.
Publication date 2010
Start page 3115
End page 3128
Total pages 14
Publisher CRC Press
Place of Publication Boca Raton, Fla.
Summary 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.
ISBN 9780849397127
Edition 3rd ed
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category B2 Book chapter in non-commercially published book
HERDC collection year 2010
Copyright notice ©2010, CRC Press
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Document type: Book Chapter
Collection: School of Information Technology
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