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In this work, we develop methods for the incremental detection of neighbor-based patterns, in particular, density-based clusters and distance-based outliers over sliding stream windows.
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Whitewater rafters might slide a stream as an ancillary activity.
This is a "Focus" gallery, and it is filled with the abstract expressionist's "Poured" paintings: gorgeous, colourful, flowing things created in the 1970s by pouring paint on canvasses in multiple layers, letting it slide and stream, pool and puddle.
Consequently, an optimized algorithm named NDFIoDS is proposed to generate non-derivable frequent itemsets over stream sliding window.
However, in dynamic data stream a sliding window is moving along the time dimension, whereas an exploratory query is moving in space over static data.
In this paper, we have studied the problem of top-k monitoring over distributed data streams in sliding window case.
We also see an apparent redesign of the video stream, wherein the camera button moves to the bottom center of the screen and remains in place as you slide down the stream.
First, some relevant statistics are extracted from the data stream using the sliding HyperLogLog algorithm.
In this paper, we propose an approximation method for mining frequent itemsets over the sliding window of a data stream.
In particular, it perceives diversification as a continuous query, in which the k most diverse results need to be evaluated for each sliding window over the data stream.
In this paper, we propose a new method (named T-HUDS) for finding top-k high utility patterns over sliding windows of a data stream.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com