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Titlebook: Database Systems for Advanced Applications; 17th International C Sang-goo Lee,Zhiyong Peng,Jaesoo Yoo Conference proceedings 2012 Springer-

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51#
發(fā)表于 2025-3-30 11:05:44 | 只看該作者
https://doi.org/10.1007/978-3-030-04981-2esents a collection of event types in a given partial order. There should be important applications to be addressed with this technology, such as monitoring stock price movements, and tracking vehicles or merchandise by using GPS or RFID sensors. EVIS employs a variant of non-deterministic finite au
52#
發(fā)表于 2025-3-30 14:37:01 | 只看該作者
53#
發(fā)表于 2025-3-30 16:53:39 | 只看該作者
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發(fā)表于 2025-3-30 21:43:51 | 只看該作者
55#
發(fā)表于 2025-3-31 01:26:12 | 只看該作者
https://doi.org/10.1007/978-3-030-04978-2s have grown very popular on the Web with sites like Amazon, Netflix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to suggest data warehouse queries and help an
56#
發(fā)表于 2025-3-31 07:44:00 | 只看該作者
0302-9743 ereed proceedings of the 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, held in Busan, South Korea, in April 2012. .The 44 revised full papers and 8 short papers presented together with 2 invited keynote papers, 8 industrial papers, 8 demo presentations, 4
57#
發(fā)表于 2025-3-31 10:49:36 | 只看該作者
58#
發(fā)表于 2025-3-31 15:39:24 | 只看該作者
Axel Schnuch,Berit Christina Carlsena large number of simulation experiments. The experimental results show that OPRS is an effective way to solve the problem of continuous probabilistic reverse skyline, and it could significantly reduce the executionx time of continuous probabilistic reverse skyline queries and meet the requirements of practical applications.
59#
發(fā)表于 2025-3-31 18:04:44 | 只看該作者
https://doi.org/10.1007/978-3-642-03827-3mendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.
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