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Can anyone recommend a method for summarizing and processing high dimensional data streams efficiently and effectively for anomaly detection?

In fact, I investigated the different methods for data stream summarization (sampling, histograms, sketches, wavelets, sliding windows) and am confused about the choice. In fact, I noticed that sampling and sliding windows are general purpose and keep the raw data, while the others are task-specific and make transformations to the data.

I am interested in the first case but it may be more consuming.

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  • $\begingroup$ Summarizing and processing is domain specific. What is your domain? $\endgroup$ Oct 23, 2020 at 13:03

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