By Michael Yap (auth.), Witold Litwin, Tore Risch (eds.)
This quantity provides the complaints of the 1st foreign convention on functions of Databases, ADB-94, held at Vadstena, Sweden in June 1994. ADB-94 supplied a different platform for the dialogue of cutting edge purposes of databases between database researchers, builders and alertness designers. The 28 refereed papers have been rigorously chosen from greater than a hundred submissions. They document on DB purposes, for instance in air site visitors, modelling, maps, atmosphere, finance, engineering, digital publishing, and electronic libraries, and they're dedicated to complicated database prone, as for instance picture textual content and multimedia modelling, fuzzy set dependent querying, wisdom administration, heterogeneous multidatabase administration, and clever networks.
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Additional info for Applications of Databases: First International Conference, ADB-94 Vadstena, Sweden, June 21–23, 1994 Proceedings
The time spent on the last page is estimated using the average time of all other pages. However, the actual time spent on each page is almost always diﬀerent from the estimation because of network traﬃc, server load, user reading speed, etc. A user proﬁler is proposed in  which uses a client side JAVA applet and can capture more accurately the time spent. However, this requires the deployment of a client side program and modiﬁcation of Web pages on the server. Besides, it is still impossible to measure the user’s actual viewing time because the person may be distracted after the page is loaded.
Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 399– 421. AAAI/MIT Press, 1996. 25, 33 12. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Printice Hall, 1988. 23 13. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990. 23 38 Yongjian Fu et al. 14. R. S. Michalski and R. Stepp. Automated construction of classiﬁcations: Conceptual clustering versus numerical taxonomy.
Chan user’s access behavior and captures his/her interests in a user proﬁle. We developed a metric for estimating interestingness of each visited page. A user proﬁle consists of two components: a Page Interest Estimator (PIE) and a Web Access Graph (WAG). Based on the content of web pages, a page interest estimator, learned from the user’s access behavior, characterizes the interests of a user. A web access graph summarizes the web page access patterns of a user. To provide personalized on-line search, our search engine consults multiple existing search engines ([38,39]), collates the returned records, and ranks them according to the user proﬁle.
Applications of Databases: First International Conference, ADB-94 Vadstena, Sweden, June 21–23, 1994 Proceedings by Michael Yap (auth.), Witold Litwin, Tore Risch (eds.)