Team Member Names: Raheleh MAKKI, Ozge YELOGLU, Axel SOTO                 T    M b N         R h l h MAKKI, O g YELOGLU, A...
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2011 Elsevier WebApp Concept Design Competition:BlobCube (First Place)


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Team: Raheleh Makki, Ozge Yeloglu, Axel Soto
Date: October 25, 2011
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2011 Elsevier WebApp Concept Design Competition:BlobCube (First Place)

  1. 1. Team Member Names: Raheleh MAKKI, Ozge YELOGLU, Axel SOTO T M b N R h l h MAKKI, O g YELOGLU, A l Faculty of Computer Science, Dalhousie University, Halifax, Canada y p , y, , HOW?Abstract Corpus l Corpus-level Vi C level View l • Th Cube visualizes up to 200 documents, where similar The C b i li t d t h i il documents are located close to each other. Blob CubeOur application, BlobCube, provides two ways of visualizing pp , ,p y g • Similarity is defined based on the frequency of three keywords y q y ythe user’s search results: document-level and corpus-level user s selected by the user. uservisualization. Th d i li ti The document-level visualization, th Bl b tl l i li ti the Blob, • Keywords and full-text of documents are retrieved from theshows the degree of relevance of four keywords to each SciVerse Content API. S iV C t t APIdocument.document The corpus level visualization the Cube focuses corpus-level visualization, Cube,on the content similiarities among the retrieved documents. g • C l represents either t Color t ith type or subject area of the documents. bj t f th d t • Size represents either recency or relevance of the documents documents.ThisThi application t g t a user who wants a f t i ight of li ti targets h t faster insight fher search results. As a result, this application brings the WHY?scientific content and the ranked list representation together ranked-list • To get a faster understanding of how retrieved documentsby employing different visualization approaches. y p y g pp differ diff r among each other. ng h th r • To offer a more comprehensive representation of the search results than traditional ranked-list.Document-levelDocument level View HOW? • Each Blob summarizes the content of a document in h l b h f d Conclusions • Bl bC b BlobCube: terms of four keywords [1] [1]. • meets the users’ need of gaining valuable insight from a g g g • Keywords and full-text of documents are retrieved full text large set of documents in a shorter time time. from the SciVerse Content API API. • runs on Hub and Science Direct. • The full text of each article is used to calculate the term full-text • h a simple and flexible interface. has i pl d fl ibl i f frequencies of the f f q f h four k y keywords. d • Even though the Blob and Cube are not novel ideas our ideas, application employs these visualizations thorough SciVerse in pp p y g an innovative way way. TFi: Normalized term frequency of keyword i Requirements: q • Bl bC b requires a J BlobCube i Java Script visualization library, such S i t i li ti lib h as d3 [2]. [ ] WHY? • O ly Elsevier journal suscribers are able to use this Only El i j l ib bl hi application to its full potential potential. • To give the user a high-level summary of a document. g g y • Keyterm frequencies can only be calculated when the user y q y • T capture additional information not represented in To p ddi i li f i p di has access to full text of the documents full-text documents. the ranked-list (subtopics) ranked list (subtopics). [1] R.M. Rohrer, J.L. Sibert, D.S. Ebert.  A Shape based Visual Interface for  [1] R.M. Rohrer, J.L. Sibert, D.S. Ebert. “A Shape‐based Visual Interface for • The Blob shape makes identifying the salient keywords Text Retrieval , IEEE Computer Graphics and Applications, 19, 40 46, 1999. Text Retrieval”, IEEE Computer Graphics and Applications, 19, 40‐46, 1999. easy to the human eye [1] [1]. [2] d3:  Data driven documents . Free Java Script [2] d3: “Data‐driven documents”. Free Java Script library for visualization