Download Advances in Web-Age Information Management: 5th by Tharam S. Dillon, Elizabeth Chang, Farookh Hussain (auth.), PDF

By Tharam S. Dillon, Elizabeth Chang, Farookh Hussain (auth.), Qing Li, Guoren Wang, Ling Feng (eds.)

This booklet constitutes the refereed court cases of the fifth foreign convention on Web-Age info administration, WAIM 2004, held in Dalian, China in July 2004.

The fifty seven revised complete papers and 23 revised brief and business papers offered including three invited contributions have been rigorously reviewed and chosen from 291 submissions. The papers are equipped in topical sections on info move processing, time sequence information processing, protection, cellular computing, cache administration, question review, net se's, XML, net providers, category, and information mining.

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Extra info for Advances in Web-Age Information Management: 5th International Conference, WAIM 2004, Dalian, China, July 15-17, 2004

Example text

In addition, let D(S,X) denote the total reduced window-accumulate-error after we changes the windows-snapshot S into S+X, where S called as the basis of X. The expression X+I means increasing the window-width of w from W(X)(w) up to W(X)(w)+∆x. Based on above analysis, the problem of adjusting window-width optimally can be described as follows. Input: M, W, Q and initial windows-snapshot S1={Min_T(w)| w∈W }. Output: The windows-increment X={∆xw | w∈W }. Object function: max D(S1,X). Subject to: ∑w∈W WY(X)(w)·Size(w)·Rate(w)≤M.

Space Efficient Quantile Summary for Constrained Sliding Windows stream 1 2 3 4 5 stream 11 00 00 11 10109 00 11 002 004 11 11 01105 100110 10016 11 00 008 150110 11 6 7 8 9 10 11 00 00 11 0 1 1 002 19 0 0 04 11 1 01105 100110 10016 11 00 1 008 150 11 0 1 11 37 12 (a) natural-form window time 1 2 3 4 5 6 7 8 9 10 (b) T-window 11 12 time Fig. 2. 2 Constrained Sliding Windows The sliding window studied in [6] can be viewed as of a natural-form. It is defined on the number of data items in the window.

The timestamp over w ranges from Now− WN(w) to Now, where Now is the current time (system clock). Let Size(w) denote tuple size and let Rate(w) denote the average rate of streaming data over w, then we can evaluate the buffer size of w with the window-width of WN(w), which is equal to WN(w)·Size(w)·Rate(w). Supposing the total available memory, named as available-memory, allocated to all sliding windows is M, then both M≥∑w∈WWN(w) · Size(w)·Rate(w) and M≥∑w∈WWY(w)·Size(w)·Rate(w) should hold at any time.

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