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» Evaluating algorithms that learn from data streams
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IPPS
2005
IEEE
15 years 3 months ago
Evaluation of Rate-Based Adaptivity in Asynchronous Data Stream Joins
Continuous query systems are an intuitive way for users to access streaming data in large-scale scientific applications containing many hundreds of streams. A challenge in these ...
Beth Plale, Nithya N. Vijayakumar
ICDE
2006
IEEE
134views Database» more  ICDE 2006»
15 years 4 months ago
Processing High-Volume Stream Queries on a Supercomputer
Scientific instruments, such as radio telescopes, colliders, sensor networks, and simulators generate very high volumes of data streams that scientists analyze to detect and under...
Erik Zeitler, Tore Risch
97
Voted
CISIS
2009
IEEE
14 years 8 months ago
Evaluation of a Framework for Dynamic Source Selection in Stream Processing
The volume of stream data delivered from different information sources is increasing. There are a variety of demands to utilize such stream data for applications. Stream processin...
Kousuke Ohki, Yousuke Watanabe, Hiroyuki Kitagawa
EDBT
2004
ACM
122views Database» more  EDBT 2004»
15 years 10 months ago
Sketch-Based Multi-query Processing over Data Streams
Abstract. Recent years have witnessed an increasing interest in designing algorithms for querying and analyzing streaming data (i.e., data that is seen only once in a fixed order) ...
Alin Dobra, Minos N. Garofalakis, Johannes Gehrke,...
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
15 years 3 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo