R
Rao Shen
Researcher at Virginia Tech
Publications - 28
Citations - 552
Rao Shen is an academic researcher from Virginia Tech. The author has contributed to research in topics: Digital library & Metadata. The author has an hindex of 14, co-authored 28 publications receiving 551 citations.
Papers
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Proceedings ArticleDOI
Convergence of knowledge management and e-learning: the GetSmart experience
TL;DR: The GetSmart system was created to apply knowledge management techniques in a learning environment based on an analysis of learning theory and the information search process and is revealing interesting knowledge representation patterns.
Proceedings ArticleDOI
Enhancing usability in CITIDEL: multimodal, multilingual, and interactive visualization interfaces
Saverio Perugini,Kathleen McDevitt,Ryan Richardson,Manuel A. Pérez-Quiñones,Rao Shen,Naren Ramakrishnan,Christopher B. Williams,Edward A. Fox +7 more
TL;DR: Four usability-enhancing interfaces to CITIDEL are described aimed at improving the user experience and supporting personalized information access by targeted communities, as well as traditional usability enhancements.
Proceedings ArticleDOI
Exploring digital libraries: integrating browsing, searching, and visualization
TL;DR: Theorems to indicate that browsing and searching can be converted or mapped to each other under certain conditions are developed and guide the design and implementation of exploring services for an integrated archaeological DL, ETANA-DL.
Book ChapterDOI
An XML Log Standard and Tool for Digital Library Logging Analysis
TL;DR: An XML-based digital library log format standard is proposed that captures a rich, detailed set of system and user behaviors supported by current digital library services and is implemented in a generic log component tool, which can be plugged into any digital library system.
Book ChapterDOI
Citiviz: A Visual User Interface to the CITIDEL System
TL;DR: Citiviz employs a dynamic hyperbolic tree to display hierarchical relationships among documents, based on where their topics fit into the ACM classification system, and provides an interactive, animated 2-dimensional scatter plot.