Adaptive, unsupervised stream mining

Adaptive, unsupervised stream mining Sensor devices and embedded processors are becoming widespread, especially in measurement/monitoring applications. Their limited resources (CPU, memory and/or communication bandwidth, and power) pose some interesting challenges. We need concise, expressive models to represent the important features of the data and that lend themselves to efficient estimation. In particular, under these severe constraints, we want models and estimation methods that (a) require little memory and a single pass over the data, (b) can adapt and handle arbitrary periodic components, and (c) can deal with various types of noise. We propose \ensuremath{\mathrm{AWSOM}} (Arbitrary Window Stream mOdeling Method), which allows sensors in remote or hostile environments to efficiently and effectively discover interesting patterns and trends. This can be done automatically, i.e., with no prior inspection of the data or any user intervention and expert tuning before or during data gathering. Our algorithms require limited resources and can thus be incorporated into sensors - possibly alongside a distributed query processing engine (10,6,27). Updates are performed in constant time with respect to stream size using logarithmic space. Existing forecasting methods (SARIMA, GARCH, etc.) and “traditional” Fourier and wavelet analysis fall short on one or more of these requirements. To the best of our knowledge, \ensuremath{\mathrm{AWSOM}} is the first framework that combines all of the above characteristics. Experiments on real and synthetic datasets demonstrate that \ensuremath{\mathrm{AWSOM}} discovers meaningful patterns over long time periods. Thus, the patterns can also be used to make long-range forecasts, which are notoriously difficult to perform. In fact, \ensuremath{\mathrm{AWSOM}} outperforms manually set up autoregressive models, both in terms of long-term pattern detection and modeling and by at least 10 x in resource consumption. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Adaptive, unsupervised stream mining

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Publisher
Springer-Verlag
Copyright
Copyright © 2004 by Springer-Verlag
Subject
ComputerScience
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-004-0130-8
Publisher site
See Article on Publisher Site

Abstract

Sensor devices and embedded processors are becoming widespread, especially in measurement/monitoring applications. Their limited resources (CPU, memory and/or communication bandwidth, and power) pose some interesting challenges. We need concise, expressive models to represent the important features of the data and that lend themselves to efficient estimation. In particular, under these severe constraints, we want models and estimation methods that (a) require little memory and a single pass over the data, (b) can adapt and handle arbitrary periodic components, and (c) can deal with various types of noise. We propose \ensuremath{\mathrm{AWSOM}} (Arbitrary Window Stream mOdeling Method), which allows sensors in remote or hostile environments to efficiently and effectively discover interesting patterns and trends. This can be done automatically, i.e., with no prior inspection of the data or any user intervention and expert tuning before or during data gathering. Our algorithms require limited resources and can thus be incorporated into sensors - possibly alongside a distributed query processing engine (10,6,27). Updates are performed in constant time with respect to stream size using logarithmic space. Existing forecasting methods (SARIMA, GARCH, etc.) and “traditional” Fourier and wavelet analysis fall short on one or more of these requirements. To the best of our knowledge, \ensuremath{\mathrm{AWSOM}} is the first framework that combines all of the above characteristics. Experiments on real and synthetic datasets demonstrate that \ensuremath{\mathrm{AWSOM}} discovers meaningful patterns over long time periods. Thus, the patterns can also be used to make long-range forecasts, which are notoriously difficult to perform. In fact, \ensuremath{\mathrm{AWSOM}} outperforms manually set up autoregressive models, both in terms of long-term pattern detection and modeling and by at least 10 x in resource consumption.

Journal

The VLDB JournalSpringer Journals

Published: Sep 1, 2004

References

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