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» Maintaining Nonparametric Estimators over Data Streams
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BTW
2005
Springer
104views Database» more  BTW 2005»
13 years 10 months ago
Maintaining Nonparametric Estimators over Data Streams
Abstract: An effective processing and analysis of data streams is of utmost importance for a plethora of emerging applications like network monitoring, traffic management, and fi...
Björn Blohsfeld, Christoph Heinz, Bernhard Se...
CIKM
2006
Springer
13 years 8 months ago
Resource-aware kernel density estimators over streaming data
A fundamental building block of many data mining and analysis approaches is density estimation as it provides a comprehensive statistical model of a data distribution. For that re...
Christoph Heinz, Bernhard Seeger
SSDBM
2006
IEEE
167views Database» more  SSDBM 2006»
13 years 11 months ago
Exploring Data Streams with Nonparametric Estimators
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...
Christoph Heinz, Bernhard Seeger
PODS
2003
ACM
143views Database» more  PODS 2003»
14 years 5 months ago
Maintaining variance and k-medians over data stream windows
The sliding window model is useful for discounting stale data in data stream applications. In this model, data elements arrive continually and only the most recent N elements are ...
Brian Babcock, Mayur Datar, Rajeev Motwani, Liadan...
SIAMCOMP
2002
152views more  SIAMCOMP 2002»
13 years 4 months ago
Maintaining Stream Statistics over Sliding Windows
We consider the problem of maintaining aggregates and statistics over data streams, with respect to the last N data elements seen so far. We refer to this model as the sliding wind...
Mayur Datar, Aristides Gionis, Piotr Indyk, Rajeev...