Application of Cloud Models in Digital Libraries

In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm measures. To overcome above disadvantages, this paper proposes two new methods based on cloud models. One is to retrieve efficien...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 7; pp. 3876 - 3879
Main Authors Hai-Yan Kang, Yan-Fang Li, Shi-Ping Tang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Algorithm performance evaluation
Application software
Cloud computing
Cloud model
Cybernetics
Digital Libraries(DLs)
Information retrieval
Information Retrieval(IR)
Machine learning
Management information systems
Search engines
Software libraries
Stability
Uncertain concept
Uncertainty
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Summary:In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm measures. To overcome above disadvantages, this paper proposes two new methods based on cloud models. One is to retrieve efficiently uncertain concepts, which can change the granularity of retrieval information. By this method it remarkably improves the recall and precision of information retrieval. Another is to evaluate efficiently retrieval algorithm, which can reflect not only average performance of an algorithm but also stability and randomicity. These two methods set up a transform of qualitative concepts and quantity. This kind of transform is carried out through strict mathematic means. Experimental data showed the method is practical. Results of evaluation will be more accurate and approach to the fact better.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370822