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VLDB
2010
ACM
144views Database» more  VLDB 2010»
13 years 6 months ago
Methods for finding frequent items in data streams
The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in d...
Graham Cormode, Marios Hadjieleftheriou
DCC
2004
IEEE
14 years 7 months ago
Fast Near-Lossless or Lossless Compression of Large 3D Neuro-Anatomical Images
3D neuro-anatomical images and other volumetric data sets are important in many scientific and biomedical fields. Since such sets may be extremely large, a scalable compression me...
Rongkai Zhao, Michael Gabriel, Geneva G. Belford
CLIMA
2004
13 years 9 months ago
The Apriori Stochastic Dependency Detection (ASDD) Algorithm for Learning Stochastic Logic Rules
Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environm...
Christopher Child, Kostas Stathis
ICDE
2007
IEEE
137views Database» more  ICDE 2007»
14 years 9 months ago
SAO: A Stream Index for Answering Linear Optimization Queries
Linear optimization queries retrieve the top-K tuples in a sliding window of a data stream that maximize/minimize the linearly weighted sums of certain attribute values. To effici...
Gang Luo, Kun-Lung Wu, Philip S. Yu
EGH
2009
Springer
13 years 5 months ago
Efficient stream compaction on wide SIMD many-core architectures
Stream compaction is a common parallel primitive used to remove unwanted elements in sparse data. This allows highly parallel algorithms to maintain performance over several proce...
Markus Billeter, Ola Olsson, Ulf Assarsson