© Anselm Spoerri Lecture 13 Housekeeping –Term Projects Evaluations –Morse, E., Lewis, M., and Olsen, K. (2002) Testing Visual Information Retrieval Methodologies Case Study: Comparative Analysis of Textual, Icon Graphical and 'Spring' Displays Journal of the American Society for Information Science and Technology (JASIST) PDFTesting Visual Information Retrieval Methodologies Case Study: Comparative Analysis of Textual, Icon Graphical and 'Spring' DisplaysPDF –Reiterer H., Mußler G., Mann T.: Visual Information Retrieval for the WWW, in: Smith M.J. et al. (eds.), Usability Evaluation and Interface Design, Lawrence Erlbaum, 2001 PDFPDF –searchCrystal Studies

© Anselm Spoerri Prototype Project –Motivate domain choice. –Perform task and need analysis. –Describe design approach and information visualization principles used. –Develop prototype. –Have an "domain expert" use the prototype and provide feedback. Class Presentation You have 15 min. to describe task analysis and your design approach. Demonstrate your prototype. Report on the "domain expert" feedback. Create Report 20 to 25 pages, written as a standard paper  10pt, double-spaced Provide screenshots of prototype and explain design approach. Include URL of prototype. Hand-in Hardcopy of report. Post report online and send instructor an with the URL.

© Anselm Spoerri Text Retrieval Visualizations – Evaluations : Morse et al. Many Tools Proposed Few Tested and Often Inconclusive / Fare Poorly Simplify Evaluation  Focus on Method (instead of implementation)  Only Static Aspects POI = Point of Interest Visualizations –Position Coding Glyph = Graphical Entity –Conveys data values via attributes such as shape, size, color

© Anselm Spoerri Glyph = Graphical Entity

© Anselm Spoerri Evaluation – Morse et al.

© Anselm Spoerri Evaluation – Morse et al. : Two-Term Boolean Test

© Anselm Spoerri Evaluation – Morse et al. : Two-Term Boolean Test

© Anselm Spoerri Evaluation – Morse et al. : Three-Term Boolean Test

© Anselm Spoerri Evaluation – Morse et al. : Vector Studies – Text List

© Anselm Spoerri Evaluation – Morse et al. : Vector Studies – Table

© Anselm Spoerri Evaluation – Morse et al. : Vector Studies – Icons

© Anselm Spoerri Evaluation – Morse et al. : Vector Studies – VIBE

© Anselm Spoerri Evaluation – Morse et al. : Vector Studies Time

© Anselm Spoerri Evaluation – Reiterer et al.

© Anselm Spoerri Evaluation – Reiterer et al.

© Anselm Spoerri Evaluation – Reiterer et al.

© Anselm Spoerri Evaluation – Reiterer et al.

© Anselm Spoerri Evaluation – Reiterer et al.

© Anselm Spoerri searchCrystal – Studies Validate Design Approach How does Overlap between Results Actually Correlate with Relevance? User Study

© Anselm Spoerri Overlap between Search Results Correlated with Relevance? Method –Use Ad-hoc track data for TREC 3, 6, 7, 8 –Systems search the SAME Database –Automatic Short Runs –50 Topics and 1,000 Documents per topic  50,000 documents –Retrieval systems can submit multiple runs  Select Best Run based Mean Average Precision TREC 319 systems 928,709 documents found TREC 624 systems 1,192,557 documents found TREC 728 systems 1,327,166 documents found TREC 835 systems 1,723,929 documents found –Compute Average by summing over all 50 topics and divide by 50

© Anselm Spoerri How does Overlap Correlate with Relevance?  Authority Effect Percentage of Documents that are Relevant Systems

© Anselm Spoerri TREC 8 – Impact of Average Rank Position?  Ranking Effect Systems Percentage of Documents that are Relevant Compute overlap structure between top 50 search results of 35 random groupings of 5 retrieval systems for 50 topics.

© Anselm Spoerri searchCrystal – Studies How does Overlap between Search Results Correlate with Relevance? Authority Effect – the more systems that find a document, the greater the probability that it is relevant Ranking Effect – the higher up a document in a ranked list and the more systems that find it, the greater the probability of its relevance  Validates searchCrystal’s Design Approach  searchCrystal Visualizes Authority & Ranking Effects  searchCrystal can Guide User’s Exploration Toward Relevant Documents

© Anselm Spoerri searchCrystal – Studies Validate Design Approach How does Overlap between Results Actually Correlate with Relevance? User Study

© Anselm Spoerri User Study – Cluster Bulls-Eye

© Anselm Spoerri User Study – RankSpiral

© Anselm Spoerri User Study – Compare Cluster Bull’s Eye and RankSpiral Nine undergraduates. Short Introduction and No Training. Randomized presentation order of data sets and display type. Subject selects ten document; Visual feedback about correct top 10 Test for Cluster Bull’s Eye and RankSpiral displays: 1) How well can novices use visual cues to find the documents that are most likely to be relevant? 2) Performance difference in terms of effectiveness and/or efficiency? 3) How much document’s distance from the display center will interfere with the size coding used to encode its probability of being relevant

© Anselm Spoerri User Study – Results Hypothesis 1: “Novices can perform the task.” Error is minimal for the top 7 documents and increases rapidly after the top 7 documents for both displays. Novice users can use the Cluster Bulls-Eye and RankSpiral displays to select highly relevant documents, especially the top 7 documents. Hypothesis 2: “RankSpiral outperforms Cluster Bulls-Eye.” 8 of the 9 subjects performed the task faster using the RankSpiral. Average time difference was 7.89 seconds. The one-sided T-test value is 0.033, which is significant at the 0.05 level. 7 out of 9 subjects performed the task more effectively using the RankSpiral. Average “relevance score” difference is The one-sided T-test value is 0.037, which is significant at the 0.05 level. Hypothesis 3: “Distance from center dominant cue.”

© Anselm Spoerri Discussion Relax searchCrystal’s design principles? –Mapping documents found by the same number of engines into the same concentric ring. Option: Distance and Size encode likelihood that a document is relevant. Internet search results: –Concentric rings are of value, because it is much harder to estimate a document’s probability of being relevant.

© Anselm Spoerri Cluster Bulls-Eye  Size = Distance from Center

© Anselm Spoerri Cluster Bulls-Eye  Size = Distance from Center

© Anselm Spoerri searchCrystal - Studies Authority & Ranking Effects Comparing Results of All Retrieval Systems at once Comparing Results of Random Subsets of Five Systems  Validating searchCrystal’s Design Principles User Study Identify Top 10 Docs in Cluster Bull’s Eye and RankSpiral  Novice Users can use the two searchCrystal displays  Statistical Difference between two displays  Distance from center is dominant visual feature

© Anselm Spoerri What is Popular on Wikipedia? Why? Please read the two papers published by me in First Monday: Approach 1 Visualize Popular Wikipedia Pages Overlap between 100 Most Visited Pages on Wikipedia for September 2006 to January 2007 Information Visualization helps to gain quick insights 2 Categorize Popular Wikipedia Pages 3 Examine Popular Search Queries 4 Determine Search Result Position of Popular Wikipedia pages 5 Implications