Browsing by Author "Chen, Shihn-Yuarn"

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Access Control in XML Repositories Using a Role-Based Approach
(2010-12-01)
Chen, Shihn-Yuarn
;
Ke, Hao-Ren
;
Tsai, Wen-Nung
;
Yang, Wei-Pang
;
資訊工程學系
Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
Access Control in XML Repositories Using a Role-Based Approach
(2010-12-01)
Chen, Shihn-Yuarn
;
Ke, Hao-Ren
;
Tsai, Wen-Nung
;
Yang, Wei-Pang
;
資訊工程學系
Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
Concept Extraction and Clustering for Search Result Organization and Virtual Community Construction
(2012-01-01)
Chen, Shihn-Yuarn
;
Chang, Chia-Ning
;
Nien, Yi-Hsiang
;
Ke, Hao-Ren
;
資訊管理與財務金融系註：原資管所+財金所
This study proposes a concept extraction and clustering method, which improves Topic Keyword Clustering by using Log Likelihood Ratio for semantic correlation and Bisection K-Means for document clustering. Two value-added services are proposed to show how this approach can benefit information retrieval (IR) systems. The first service focuses on the organization and visual presentation of search results by clustering and bibliographic coupling. The second one aims at constructing virtual research communities and recommending significant papers to researchers. In addition to the two services, this study conducts quantitative and qualitative evaluations to show the feasibility of the proposed method; moreover, comparison with the previous approach is also performed. The experimental results show that the accuracy of the proposed method for search result organization reaches 80%, outperforming Topic Keyword Clustering. Both the precision and recall of virtual community construction are higher than 70%, and the accuracy of paper recommendation is almost 90%.
Social Tagging in Digital Archives
(2008)
Chen, Shihn-Yuarn
;
Teng, Yu-Ying
;
Ke, Hao-Ren
;
資訊工程學系
;
Department of Computer Science
Social trend tracking by time series based social tagging clustering
(2011-09-15)
Chen, Shihn-Yuarn
;
Tseng, Tzu-Ting
;
Ke, Hao-Ren
;
Sun, Chuen-Tsai
;
資訊工程學系
Social tagging is widely practiced in the Web 2.0 era. Users can annotate useful or interesting Web resources with keywords for future reference. Social tagging also facilitates sharing of Web resources. This study reviews the chronological variation of social tagging data and tracks social trends by clustering tag time series. The data corpus in this study is collected from Hemidemi.com. A tag is represented in a time series form according to its annotating Web pages. Then time series clustering is applied to group tag time series with similar patterns and trends in the same time period. Finally, the similarities between clusters in different time periods are calculated to determine which clusters have similar themes, and the trend variation of a specific tag in different time periods is also analyzed. The evaluation shows the recommendation accuracy of the proposed approach is about 75%. Besides, the case discussion also proves the proposed approach can track the social trends. (C) 2011 Elsevier Ltd. All rights reserved.