By Charu C. Aggarwal
This ebook essentially discusses matters with regards to the mining points of knowledge streams and it really is particular in its basic concentrate on the topic. This quantity covers mining features of knowledge streams comprehensively: every one contributed bankruptcy incorporates a survey at the subject, the main principles within the box for that exact subject, and destiny learn instructions. The booklet is meant for a certified viewers composed of researchers and practitioners in undefined. This e-book is usually acceptable for advanced-level scholars in computing device technology.
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Additional resources for Data Streams: Models and Algorithms
While the first two results are quite easy to see, the last one needs to be proven formally. 2 Let h be a user-speciJiedtime window, t, be the current time, and t, be the time of the last stored snapshot of any orderjust before the time t, - h. Then t, - t, 5 2 . h. Proof: Let r be the smallest integer such that ar 2 h. Therefore, we know that ar-I < h. Since we know that there are a+ 1snapshots of order (r- I), at least one snapshot of order r -1must always exist before t, -h. Lett, be the snapshot of order r - 1 which occurs just before t, - h.
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In this technique, snapshots are classified into differentframe numbers which can vary from 0 to a value no larger than log2(T), where T is the maximum length of the stream. The frame number of a particular class of snapshots defines the level of granularity in time at which the snapshots are maintained. Specifically, snapshots of frame number i are stored at clock times which are divisible by 2i, but not by 2i+1. Therefore, snapshots of frame number 0 are stored only at odd clock times. It is assumed that for each frame number, at most max-capacity snapshots are stored.
Data Streams: Models and Algorithms by Charu C. Aggarwal