Dependable real-time data mining

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Author

Christopher Clifton

Tech report number

CERIAS TR 2005-133

Entry type

conference

Abstract

n this paper we discuss the need for real-time data mining for many applications in government and industry and describe resulting research issues. We also discuss dependability issues including incorporating security, integrity, timeliness and fault tolerance into data mining. Several different data mining outcomes are described with regard to their implementation in a real-time environment. These outcomes include clustering, association-rule mining, link analysis and anomaly detection. The paper describes how they would be used together in various parallel-processing architectures. Stream mining is discussed with respect to the challenges of performing data mining on stream data from sensors. The paper concludes with a summary and discussion of directions in this emerging area.

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Date

2005 – 05

URL

http://ieeexplore.ieee.org/sea ... pper.jsp?arnumber=1420965

Address

Seattle, Washington

Key alpha

Clifton

Note

8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005) May 18-20, 2005 in Seattle Washington

Publication Date

2005-05-01

BibTex-formatted data

To refer to this entry, you may select and copy the text below and paste it into your BibTex document. Note that the text may not contain all macros that BibTex supports.

@Conference{ Clifton,
	title = "Dependable real-time data mining",
	author = "Christopher Clifton",
	year = "2005",
	month = "05",
	address = "Seattle, Washington",
	note = "8th IEEE
International Symposium on Object-Oriented Real-Time Distributed
Computing (ISORC 2005)
May 18-20, 2005 in Seattle Washington",
	abstract = "n this paper we discuss the need for real-time data mining for many applications in government and industry and describe resulting research issues. We also discuss dependability issues including incorporating security, integrity, timeliness and fault tolerance into data mining. Several different data mining outcomes are described with regard to their implementation in a real-time environment. These outcomes include clustering, association-rule mining, link analysis and anomaly detection. The paper describes how they would be used together in various parallel-processing architectures. Stream mining is discussed with respect to the challenges of performing data mining on stream data from sensors. The paper concludes with a summary and discussion of directions in this emerging area.",
	url = "http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1420965",
}