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Published Articles >> Table of Contents >> Abstract
18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
pp. 261-264
Exploring Data Streams with Nonparametric Estimators
Christoph Heinz, Philipps University Marburg, Germany
Bernhard Seeger, Philipps University Marburg, Germany
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SSDBM.2006.25
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| Abstract |
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A variety of real-world applications requires a meaningful
online analysis of transient data streams. An important
building block of many analysis tasks is the characterization
of the underlying data distribution. Sophisticated
techniques from the area of nonparametric statistics provide
a well-defined estimation of continuous data distributions.
The analysis of data streams may gain advantage
of these techniques, however, the rigid processing requirements
of streams render a direct application impossible. In
our work, we tackle the adaptation of nonparametric techniques
to streaming data. We concentrate on density estimation
as it provides a convenient basis for the exploration
of an unknown continuous data distribution. Specifically,
we have developed kernel- and wavelet-based density estimators
for data streams in compliance with their processing
requirements. Both techniques are incorporated into
PIPES, our Java library for advanced data stream processing
and analysis. In the demonstration, we will present
our nonparametric density estimators over data streams
and show their performance for a variety of heterogeneous
data streams from different real-world application scenarios.
We will also present the implementation of further
analysis tasks on top of our estimators by means of illustrative
use cases.
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 Additional Information
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Citation:
Christoph Heinz, Bernhard Seeger,
"Exploring Data Streams with Nonparametric Estimators,"
ssdbm,
pp. 261-264,
18th International Conference on Scientific and Statistical Database Management (SSDBM'06),
2006
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