By Yang Bo, Li Yingfang, Li Junsheng, Sun Jianhong (auth.), Yanwen Wu (eds.)
This six-volume-set (CCIS 231, 232, 233, 234, 235, 236) constitutes the refereed lawsuits of the overseas convention on Computing, info and regulate, ICCIC 2011, held in Wuhan, China, in September 2011. The papers are equipped in volumes on cutting edge Computing and data (CCIS 231 and 232), volumes on Computing and clever structures (CCIS 233 and 234), and in volumes on info and administration Engineering (CCIS 235 and 236).
Read or Download Computing and Intelligent Systems: International Conference, ICCIC 2011, Wuhan, China, September 17-18, 2011. Proceedings, Part IV PDF
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Extra resources for Computing and Intelligent Systems: International Conference, ICCIC 2011, Wuhan, China, September 17-18, 2011. Proceedings, Part IV
0003292316979 Table 2. 5 5 -3 x 10 Fig. 4. 5 5 -3 x 10 Fig. 5. Correlation between the percentage of representative TB against personal TBs and the ratio of selecting representative tagging users All the results approved that the EC is suitable for measuring the representativeness of the tagging users and TBs. Furthermore, representative users with high EC are tend to perform representative TBs. The higher EC a tagging user has, the more portion of his TBs are representative. Therefore, a conclusion can be safely drawn that the proposed strategy and the algorithm are effective.
3 Proposed Strategy and Algorithm A. Strategy Description The proposed strategy is very simple: the tags used by representative tagging users should be considered with high priority for constructing item’s tag-based model. The representative tagging users are those whose tag usage solutions, that is tag-item pairs referred as tagging behaviors, are concordant with the common sense hold by the majority of all the tagging users. To apply the strategy, the definitions and the algorithm below is introduced.
If p=50%, 1GB data have 4,000,000 illegal tuples. Every relation has a same p value. Set p=0,1,5,10,20 and 50. Experiments should consider high p value to test the method. The number of projection attributes and aggregation attributes in queries. Table 4 shows that rewritten queries are longer than original queries in execution time. Since Q1 has more aggregation and projection attributes, its execution time is longer than Q6. The overload is affected by the result sizes of result original queries without grouping.
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