UMD CMSC 838S - Document Visualization (24 pages)

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Document Visualization



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Document Visualization

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Pages:
24
School:
University of Maryland, College Park
Course:
Cmsc 838s - Advanced Topics In Programming Languages

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Document Visualization Visualizing the Non Visual Spatial Analysis and Interaction with Information from Text Documents J A Wise J J Thomas K Pennock D Lantrip M Pottier A Schur and V Crow Proceedings of Infoviz 95 Reviewed by Nada Golmie for CMSC 838S Fall 1999 Outline Document visualization What Why How Examples for 1D 2D visualizations vector space analysis Salton 1995 reduced text interaction Eick 1992 2D maps of document collections Lin 1992 3D Visualization SPIRE Wise et al 1995 3D Time Interactive Landscapes Rennison 1994 01 14 19 Document Visualization 2 Document Visualization Document visualization is an important IV application due to emerging technology trends World Wide Web Digital Libraries Communication Advances Mapping a text document Understand the content of a document Mapping a collection of documents Discover relationships among documents 01 14 19 Document Visualization 3 Vector Space Analysis Salton et al Support of free form text queries in IR Text passages are mapped into a vector of terms in high dimensional space Di d i1 d i2 d ik where d ik is the weighted assigned to term k in document Di Given document Di and queryQ j a similarity computation is computedt as sim Di Q j d ik d jk k 1 01 14 19 Document Visualization 4 Reduced Text Interaction SeeSoft Eick 1992 Reduced representation display of lines as rows files as columns max 900 rows per column Colors are used to display statistics statistics include age programmer feature type of line number of times the line was executed Direct manipulation techniques find interesting patterns capability to read actual code using magnification 01 14 19 Document Visualization 5 SeeSoft Eick 1992 01 14 19 Document Visualization 6 2D Maps Lin 1992 Framework for information retrieval mapping of high dimensional document space into 2D map document relationships are explored using visual cues such as dots links clusters and areas Neural network self organizing learning algorithm based on Kohonen s feature map



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