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» Approximate data mining in very large relational data
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DAWAK
1999
Springer
15 years 6 months ago
Modeling KDD Processes within the Inductive Database Framework
One of the most challenging problems in data manipulation in the future is to be able to e ciently handle very large databases but also multiple induced properties or generalizatio...
Jean-François Boulicaut, Mika Klemettinen, ...
SDM
2007
SIAM
131views Data Mining» more  SDM 2007»
15 years 3 months ago
Load Shedding in Classifying Multi-Source Streaming Data: A Bayes Risk Approach
In many applications, we monitor data obtained from multiple streaming sources for collective decision making. The task presents several challenges. First, data in sensor networks...
Yijian Bai, Haixun Wang, Carlo Zaniolo
SENSYS
2004
ACM
15 years 7 months ago
Medians and beyond: new aggregation techniques for sensor networks
Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), makin...
Nisheeth Shrivastava, Chiranjeeb Buragohain, Divya...
ICDE
2009
IEEE
171views Database» more  ICDE 2009»
16 years 3 months ago
A Framework for Clustering Massive-Domain Data Streams
In this paper, we will examine the problem of clustering massive domain data streams. Massive-domain data streams are those in which the number of possible domain values for each a...
Charu C. Aggarwal
GIS
2007
ACM
16 years 2 months ago
An interactive framework for raster data spatial joins
Many Geographic Information System (GIS) applications must handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries i...
Wan D. Bae, Petr Vojtechovský, Shayma Alkob...