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» Streaming with causality: a practical approach
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ICDM
2010
IEEE
199views Data Mining» more  ICDM 2010»
14 years 7 months ago
Addressing Concept-Evolution in Concept-Drifting Data Streams
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two s...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu ...
DATE
2007
IEEE
117views Hardware» more  DATE 2007»
15 years 3 months ago
Resource prediction for media stream decoding
Resource prediction refers to predicting required compute power and energy resources for consuming a service on a device. Resource prediction is extremely useful in a client-serve...
Juan Hamers, Lieven Eeckhout
103
Voted
ADC
2008
Springer
156views Database» more  ADC 2008»
15 years 3 months ago
Interactive Mining of Frequent Itemsets over Arbitrary Time Intervals in a Data Stream
Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches as...
Ming-Yen Lin, Sue-Chen Hsueh, Sheng-Kun Hwang
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
15 years 9 months ago
Real-time ranking with concept drift using expert advice
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
Hila Becker, Marta Arias
CSFW
1992
IEEE
15 years 1 months ago
Secure Dependencies with Dynamic Level Assignments
Most security models explicitly (or implicitly) include the tranquillity principle which prohibits changing the security level of a given piece of information. Yet in practical sy...
Pierre Bieber, Frédéric Cuppens