A survey of stream data mining
Date Issued
2007
Author(s)
Gjorgjevikj, Dejan
Abstract
At present a growing number of
applications that generate massive streams of data
need intelligent data processing and online
analysis. Real-time surveillance systems,
telecommunication systems, sensor networks and
other dynamic environments are such examples.
The imminent need for turning such data into
useful information and knowledge augments the
development of systems, algorithms and
frameworks that address streaming challenges.
The storage, querying and mining of such data
sets are highly computationally challenging tasks.
Mining data streams is concerned with extracting
knowledge structures represented in models and
patterns in non stopping streams of information.
In this paper, we present the theoretical
foundations of data stream analysis and identify
potential directions of future research. Mining
data stream techniques are being critically
reviewed.
applications that generate massive streams of data
need intelligent data processing and online
analysis. Real-time surveillance systems,
telecommunication systems, sensor networks and
other dynamic environments are such examples.
The imminent need for turning such data into
useful information and knowledge augments the
development of systems, algorithms and
frameworks that address streaming challenges.
The storage, querying and mining of such data
sets are highly computationally challenging tasks.
Mining data streams is concerned with extracting
knowledge structures represented in models and
patterns in non stopping streams of information.
In this paper, we present the theoretical
foundations of data stream analysis and identify
potential directions of future research. Mining
data stream techniques are being critically
reviewed.
Subjects
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