Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/38935
DC Field | Value | |
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dc.title | Scaling SDI systems via query clustering and aggregation | |
dc.contributor.author | Zhang, X. | |
dc.contributor.author | Yang, L.H. | |
dc.contributor.author | Lee, M.L. | |
dc.contributor.author | Hsu, W. | |
dc.date.accessioned | 2013-07-04T07:30:14Z | |
dc.date.available | 2013-07-04T07:30:14Z | |
dc.date.issued | 2004 | |
dc.identifier.citation | Zhang, X.,Yang, L.H.,Lee, M.L.,Hsu, W. (2004). Scaling SDI systems via query clustering and aggregation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2973 : 208-219. ScholarBank@NUS Repository. | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/38935 | |
dc.description.abstract | XML-based Selective Dissemination of Information (SDI) systems aims to quickly deliver useful information to the users based on their profiles or user subscriptions. These subscriptions are specified in the form of XML queries. This paper investigates how clustering and aggregation of user queries can help scale SDI systems by reducing the number of document-subscription matchings required. We design a new distance function to measure the similarity of query patterns, and develop a filtering technique called YFilter* that is based on YFilter. Experiment results show that the proposed approach is able to achieve high precision, high recall, while reducing runtime requirement. © Springer-Verlag 2004. | |
dc.source | Scopus | |
dc.type | Article | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.description.volume | 2973 | |
dc.description.page | 208-219 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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