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» STREAM: The Stanford Stream Data Manager
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KDD
2008
ACM
217views Data Mining» more  KDD 2008»
14 years 8 months ago
Stream prediction using a generative model based on frequent episodes in event sequences
This paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wis...
Srivatsan Laxman, Vikram Tankasali, Ryen W. White
ICDE
2008
IEEE
144views Database» more  ICDE 2008»
14 years 9 months ago
Fast and Highly-Available Stream Processing over Wide Area Networks
Abstract-- We present a replication-based approach that realizes both fast and highly-available stream processing over wide area networks. In our approach, multiple operator replic...
Jeong-Hyon Hwang, Stanley B. Zdonik, Ugur Ç...
CIKM
2010
Springer
13 years 6 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An
MOBISYS
2007
ACM
14 years 7 months ago
A time-and-value centric provenance model and architecture for medical event streams
Provenance becomes a critical requirement for healthcare IT infrastructures, especially when pervasive biomedical sensors act as a source of raw medical streams for large-scale, a...
Min Wang, Marion Blount, John Davis, Archan Misra,...
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
14 years 8 months ago
A fast algorithm for finding frequent episodes in event streams
Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the...
Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan