In PODS 2003, Babcock, Datar, Motwani and O’Callaghan [4] gave the first streaming solution for the k-median problem on sliding windows using O( k τ4W 2τ log2 W ) space, with a O(2O(1/τ)) approximation factor, where W is the window size and τ ∈ (0, 2 ) is a user-specified parameter. They left as an open question whether it is possible to improve this to polylogarithmic space. Despite much progr...

To e ciently support continuous display for continuous media many approaches based on the striping strategy that is implemented on a multi disk drive have been proposed However the striping strategy can only support simultaneous display of continuous media which are predetermined before they are stored in the multi disk drive For an interactive display application a system must support users to...

Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transfor...

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. The existing sliding window-based HUP mining algorithms over stream data suffer from the level-wise candidate generationand-test problem. Therefore, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve the...

In order to improve the prediction accuracy of chaotic time series, a chaotic time series forecasting method based on online weighted least squares support vector machine regression (WLS-SVM) is proposed. In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system. The model of the system is refreshed on-line with th...

Clustering queries over sliding windows require maintaining cluster memberships that change as windows slide. To address this, the Generic 2-phase Continuous Summarization framework (G2CS) utilizes a generation based window maintenance approach where windows are maintained over different time intervals. It provides algorithm independent and efficient sliding mechanisms for clustering queries wh...