Recent years have witnessed an increasing number of studies in stream mining, which aim at building an accurate model for continuously arriving data. Somehow most existing work ma...
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
We introduce a new domain-independent framework for formulating and efficiently evaluating similarity queries over historical data, where given a history as a sequence of timestam...
We propose Virtual Full Replication by Adaptive segmentation (ViFuR-A), and evaluate its ability to maintain scalability in a replicated real-time database. With full replication ...
In this paper we present a method for the integration of nonlinear holonomic constraints in deformable models and its application to the problems of shape and illuminant direction...