By Masaru Kitsuregawa, Masashi Toyoda (auth.), Haixun Wang, Shijun Li, Satoshi Oyama, Xiaohua Hu, Tieyun Qian (eds.)
This e-book constitutes the refereed court cases of the twelfth foreign convention on Web-Age details administration, WAIM 2011, held in Wuhan, China in September 2011.
The fifty three revised complete papers awarded including abstracts and one complete paper of the keynote talks have been rigorously reviewed and chosen from a complete of 181 submissions. The papers are geared up in topical sections on question processing, doubtful information, socialmedia, semantics, info mining, cloud info, multimedia information, consumer versions, information administration, graph info, identify disambiguation, functionality, temporal facts, XML, spatial information and occasion detection.
Read Online or Download Web-Age Information Management: 12th International Conference, WAIM 2011, Wuhan, China, September 14-16, 2011. Proceedings PDF
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Extra resources for Web-Age Information Management: 12th International Conference, WAIM 2011, Wuhan, China, September 14-16, 2011. Proceedings
Example text
In addition to works on keyword search over traditional relational databases, there are many existing works on other types of structured data. [15] discusses the problem of performing keyword search on relational data streams. [7] addresses the issue of keyword search on relational databases with star-schemas commonly found in OLAP applications, where the focus is finding the relevant data subspaces rather than joining networks. [16] introduces keyword search over the spatial database. Keyword search over XML data has also attracted a great deal of attention [12,14].
11. Time taken by different size of Uniform and Zipf datasets To study the effect of the dataset cardinality, we use the same data distribution, same dimensionality and different cardinalities datasets. In these experiments, k takes 8, the cardinalities of 2D datasets (Uniform and Zipf) range from 10000 to 40000. 11 shows the query time increases with the cardinalities growing, because the height of the R*-tree increases with the cardinalities growing. RkNN with Moving Query Point Above having demonstrated the efficiency of BRC for RkNN queries with static query point, we proceed to evaluate C-BRC for C-RkNN.
The query terms are sorted based on their frequency. Similar frequencies, where the frequency difference between the corresponding terms is below the predefined threshold, are treated as having the same rank. It is believed that segments with higher ranks are often preferred by users because they are chosen more often by users. Therefore, we consider the score of a segment to be inversely proportional to its rank. Hence, the second term added to the ranking function is as follows: scoreLog2 (S) = 1 1 + log(r) (3) Here r is the rank of a segment.