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

Loading next page...
 
/lp/springer_journal/adaptive-unsupervised-stream-mining-DukbUSJbKA
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

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial