Advances in Multimedia, Software Engineering and Computing by Hanxiang Wu (auth.), David Jin, Sally Lin (eds.)

By Hanxiang Wu (auth.), David Jin, Sally Lin (eds.)

MSEC2011 is an built-in convention concentrating its concentration upon Multimedia, software program Engineering, Computing and schooling. within the continuing, you could research even more wisdom approximately Multimedia, software program Engineering ,Computing and schooling of researchers everywhere in the global. the most function of the continuing is for use as an alternate pillar for researchers who're operating within the pointed out box. with a purpose to meet excessive general of Springer, AISC sequence ,the association committee has made their efforts to do the subsequent issues. first of all, bad caliber paper has been refused after reviewing path by means of nameless referee specialists. Secondly, periodically overview conferences were held round the reviewers approximately 5 instances for changing reviewing feedback. eventually, the convention association had numerous initial periods sooner than the convention. via efforts of other humans and departments, the convention may be winning and fruitful.

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Additional resources for Advances in Multimedia, Software Engineering and Computing Vol.2: Proceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing, November 26–27, Wuhan, China

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Query was also transformed into Finite Semantic Graph, and semantic matched with full graph. Experiment has shown that algorithm is effective. Keywords: big data, semantic, map/reduce. 1 Introduction Big Data [1] is not new concept, which was talked about again and again by Bill Inmon in 1990s. But it becomes hot spot in recent years. That’s due to development of Internet, cloud computing and Internet of things. With these developments, mass data are produced continually. These data are too big to be processed effectively.

The window size is 4. 2, P2=1/2. (b) Read four transactions, and calculate transaction number Dl=K*Pl=1 and D2=K*P2=1 that support it to be deleted of two transactions AB and AC. (c) Compute hidden efficiency of three restrictive transactions in the database, we can get Table 2. Table 2. Hidden efficiency of transaction items Transaction transaction1 transaction2 transaction3 A 5/1 5/2 5/1 Items B 2/1 2/1 C 2/1 2/1 (d) Select the item with lowest hidden efficiency to delete, so we can delete item B in transaction 1.

The DUOPPDM algorithm introduces concept of window, making it no longer is a algorithm based on memory and can be used for large-scale database. The concept of window firstly appeared in [7]. Suppose the window size is K, the algorithm flow is as following: Step 1: Compute transaction number Num[1:n] of each restrictive itemset. Step 2: Compute the number need to be deleted DelNum[1:n] of each restrictive itemset and the proportion of each itemset in total transaction number, namely openness p[l: n].

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