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In a larger time window, more distinct destination ports can be seen.
For the sliding HyperLogLog algorithm, a larger time window W′=5 min can be added.
Moreover, a larger time window results in higher memory consumption and processing.
One can easily check that it is a false positive using Figure 6 which displays the aggregated input data over larger time window.
There is clearly a tradeoff in the choice of the size of the sliding time window W. With a larger time window, one can answer requests concerning larger durations, for example, the number of destination ports in the last 30 min. But to deal with a larger time window, more information has to be stored.
A further consideration here is the time categories used are not equal in terms of the total number of hours available, some have a larger time window within which theft could be committed.
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So, one has to check that the total used memory remains reasonable for larger time windows.
This demonstrates that our adaptive adjustment approach through reinforcement learning works in larger time windows as well.
In these studies we used datasets covering larger time windows (from one to several centuries), and then more suitable for our investigations.
Although larger time windows are certainly possible (and their effects interesting to investigate), in this work we are less concerned with the optimal time-window size and consistently use the above described approach.
However, there may be some flexibility in the visit schedule with larger time windows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com