NDLI: AGILE: a general approach to detect transitions in evolving data streams

Content Provider	IEEE Xplore Digital Library
Author	Jiong Yang Wei Wang
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Case Western Reserve Univ., Cleveland, OH, USA (Jiong Yang)
Abstract	In many applications such as e-commerce, system diagnosis and telecommunication services, data arrives in streams at a high speed. It is common that the underlying process generating the stream may change over time, either as a result of the fundamental evolution or in response to some external stimulus. Detecting these changes is a very challenging problem of great practical importance. The overall volume of the stream usually far exceeds the available main memory and access to the data stream is typically performed via a linear scan in ascending order of the indices of the records. In this paper, we propose a novel approach, AGILE, to monitor streaming data and to detect distinguishable transitions of the underlying processes. AGILE has many advantages over the traditional Hidden Markov Model, e.g., AGILE only requires one scan of the data.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	559
Ending Page	562
File Size	130070
Page Count	4
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Monitoring Data mining Intrusion detection Computer science Application software Telecommunication services Data analysis Change detection algorithms Process design Emission tree Stream processing Transition detection Variable memory Markov model
Content Type	Text
Resource Type	Article

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