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研究生:詹子儀
研究生(外文):Tzu-Yi Chan
論文名稱:視覺化情境式多維度瀏覽
論文名稱(外文):Visualization Support to Contextual Multi-dimensional Browse
指導教授:莊裕澤莊裕澤引用關係
指導教授(外文):Yuh-Jzer Joung
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:99
中文關鍵詞:資訊視覺化多維度視覺化情境式瀏覽
外文關鍵詞:Information visualizationMulti-dimensional visualizationContextual browse
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利用視覺化方法(visualization techniques)來呈現多維度資訊(multi-dimensional information)能幫助使用者更有效地瀏覽,這些方法包含了sliding rods、star coordinates和parallel bargrams都能清楚地呈現每個維度以及所有的項目。然而,這些方法也還是有些缺點,他們都主要著重在每個項目上,而沒有呈現出其他像是維度間關係的資訊,並且,這樣的呈現會受限於資料量大小及維度的數目而無法呈現大量的資訊。

因此為了解決以上的問題,本研究提出了一個hyperbolic-like點線圖的設計,其中每個節點代表一個維度。為了能選擇最好的呈現方式,我們整理了學者的研究關於不同圖形屬性(graphical attribute)傳遞不同型態資料的效果,之後我們對受試者做實驗,觀察他們對於不同圖形屬性有何不同的看法。經過一系列的實驗,我們發現利用節點顏色(node color hue)來呈現不同類別的維度、線段長短(edge length)呈現維度不同的重要性、節點顏色深淺(node color saturation)來呈現資料量大小為最有效。此外,我們也證明了圖形的呈現更能表現多維度資訊相對於以文字為主的介面。
To use visualization techniques to display multi-dimensional information helps users browse effectively. Among of them such as sliding rods, star coordinate, and parallel bargrams, present each dimension with all items clearly. However, these still can be improved. Most of them focus on an individual item without the relations between dimensions. Besides, the display is limited by
the size of the data sets and the number of the dimensions.



In this paper we propose a hyperbolic-like nodes and edges
diagram, where a node represents a dimension. In order to choose the best display, we arrange previous studies of the effectiveness of the graphical attributes for conveying different types of information. Afterwards, we conduct a within-subject empirical study of the effectiveness of our conclusion. Through experiments, we find that node color hue, edge length, and node color
saturation are effective to encode dimension classification, dimension importance, and data volume effectively. In addition, we prove that the graphical display is much better than the text-based one to present multi-dimensional information.
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Work 5
2.1 Browse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Browsing Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1 Single Hierarchical Browse . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 Multi-Dimensional Browse . . . . . . . . . . . . . . . . . . . . . 8
2.2.2.1 Concept Hierarchies . . . . . . . . . . . . . . . . . . . 9
2.2.2.2 Multi-faceted . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2.3 View-based . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Brief Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Visualization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Space Filling Techniques . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 Node Link Techniques . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 Better Uses of Screen Space . . . . . . . . . . . . . . . . . . . . 19
2.3.3.1 Hyperbolic View . . . . . . . . . . . . . . . . . . . . . 19
2.3.3.2 Zoomable User Interface . . . . . . . . . . . . . . . . . 22
2.3.3.3 Large Tree Visualization . . . . . . . . . . . . . . . . . 23
2.3.3.4 Hybrid of Treemaps and Node-Link Diagrams . . . . . 26
2.3.4 Multi-dimensional Visualization . . . . . . . . . . . . . . . . . . 27
2.3.4.1 Parallel Bargrams . . . . . . . . . . . . . . . . . . . . 27
2.3.4.2 Star Coordinates . . . . . . . . . . . . . . . . . . . . . 28
2.3.4.3 Sliding Rods . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.5 Brief Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4 Survey Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 Empirical Study 31
3.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Attributed Information Visualization . . . . . . . . . . . . . . . . . . . 31
3.2.1 Information Attributes . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.2 Graphical Attributes . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.2.1 Scale of Property . . . . . . . . . . . . . . . . . . . . . 33
3.2.2.2 Graphical Encoding . . . . . . . . . . . . . . . . . . . 33
3.2.3 GraphicalMapping . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2.4 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.1 Test Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.2 Method : Experiment 1 . . . . . . . . . . . . . . . . . . . . . . 39
3.3.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.2.2 Platform and Materials . . . . . . . . . . . . . . . . . 41
3.3.2.3 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.2.4 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.3 Method : Experiment 2 . . . . . . . . . . . . . . . . . . . . . . 43
3.3.3.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.3.2 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.3.3 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.4 Method : Experiment 3 . . . . . . . . . . . . . . . . . . . . . . 44
3.3.4.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.4.2 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.4.3 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Results and Discussion 46
4.1 Results of Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.1 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.2 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.1.3 Experiment 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Analysis of Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2.1 Analysis of Response Time . . . . . . . . . . . . . . . . . . . . . 55
4.2.1.1 Experiment 1: Design . . . . . . . . . . . . . . . . . . 56
4.2.1.2 Experiment 1: ExperimentalModel . . . . . . . . . . . 56
4.2.1.3 Experiment 1: Analysis of Variance . . . . . . . . . . . 57
4.2.1.4 Experiment 2: Design . . . . . . . . . . . . . . . . . . 58
4.2.1.5 Experiment 2: ExperimentalModel . . . . . . . . . . . 60
4.2.1.6 Experiment 2: Analysis of Variance . . . . . . . . . . . 60
4.2.1.7 Experiment 3: Design . . . . . . . . . . . . . . . . . . 62
4.2.1.8 Experiment 3: ExperimentalModel . . . . . . . . . . . 63
4.2.1.9 Experiment 3: Analysis of Variance . . . . . . . . . . . 63
4.2.2 Analysis of Satisfaction . . . . . . . . . . . . . . . . . . . . . . . 64
4.2.2.1 Experiment 1: Design . . . . . . . . . . . . . . . . . . 64
4.2.2.2 Experiment 1: ExperimentalModel . . . . . . . . . . . 66
4.2.2.3 Experiment 1: Analysis of Variance . . . . . . . . . . . 66
4.2.2.4 Experiment 2: Design . . . . . . . . . . . . . . . . . . 68
4.2.2.5 Experiment 2: ExperimentalModel . . . . . . . . . . . 68
4.2.2.6 Experiment 2: Analysis of Variance . . . . . . . . . . . 68
4.2.2.7 Experiment 3: Design . . . . . . . . . . . . . . . . . . 69
4.2.2.8 Experiment 3: ExperimentalModel . . . . . . . . . . . 70
4.2.2.9 Experiment 3: Analysis of Variance . . . . . . . . . . . 70
5 Conclusion and Future Work 73
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Bibliography 75
A Experiment 1 82
A.1 Dimension Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 82
A.2 Dimension Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
A.3 Data Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
B Experiment 2 92
C Experiment 3 97
[1] Benjamin B. Bederson. PhotoMesa: A zoomable image browser using quantum
treemaps and bubblemaps. In UIST ’01: Proceedings of the 14th Annual ACM
Symposium on User Interface Software and Technology, pages 71–80, New York,
NY, United States, November 2001. ACM Press.
[2] Benjamin B. Bederson and James D. Hollan. Pad++: A zooming graphical interface
for exploring alternate interface physics. In UIST ’94: Proceedings of the Seventh
Annual ACM Symposium on User Interface Software and Technology, pages
17–26, New York, NY, United States, November 1994. ACM Press.
[3] Benjamin B. Bederson, Ben Shneiderman, and Martin Wattenberg. Ordered and
quantum treemaps: Making effective use of 2D space to display hierarchies. ACM
Transaction on Graphics, 21(4):833–854, October 2002.
[4] Car and Driver.com. http://www.caranddriver.com/. Retrieved September 2005
from http://www.caranddriver.com/.
[5] Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, editors. Readings in
information visualization: using vision to think. Morgan Kaufmann Publishers
Inc., San Francisco, CA, United States, 1999.
[6] Erran Carmel, Stephen Crawford, and Hsinchun Chen. Browsing in hypertext: a
cognitive study. IEEE Transactions on Systems, Man, and Cybernetics, 22(5):865–
884, September/October 1992.
[7] Hsinchun Chen, Andrea L. Houston, Robin R. Sewell, and Bruce R. Schatz. Internet
browsing and searching: User evaluations of category map and concept space
techniques. Journal of the American Society for Information Science, 49(7):582–
603, May 1998.
[8] Gouthami Chintalapani, Catherine Plaisant, and Ben Shneiderman. Extending
the utility of treemaps with flexible hierarchy. In IV ’04: Proceedings of the Information
Visualisation, Eighth International Conference on (IV’04), pages 335–344,
Washington, DC, United States, July 2004. IEEE Computer Society.
[9] William S. Cleveland and Robert McGill. Graphical perception: Theory, experimentation,
and application to the development of graphical methods. Journal of
the American Statistical Association, 79(387):531–554, September 1984.
[10] TheBrain Technologies Corp. http://www.thebrain.com/. Retrieved June 2006
from http://www.thebrain.com/.
[11] J. F. Cove and B. C. Walsh. Online text retrieval via browsing. Information
Processing and Management, 24(1):31–37, 1988.
[12] Abe Crystal and Paula Land. Metadata and search. In
Dublin Core Metadata Initiative, September 28, 2003.
http://dublincore.org/groups/corporate/Seattle/, September 2003.
[13] Abe Crystal and Paula Land. Metadata and search: Global corporate circle
DCMI 2003 workshop. In Dublin Core Metadata Initiative, September 28, 2003.
http://dublincore.org/groups/corporate/Seattle/, September 2003.
[14] dmoz open directory project. About the open directory project. Retrieved June
2006 from http://dmoz.org/about.html.
[15] Peter Eklund, Nataliya Roberts, and Steve Green. OntoRama: Browsing RDF
ontologies using a hyperbolic-style browser. In CW ’02: Proceedings of the First
International Symposium on Cyber Worlds (CW’02), pages 405–411, Washington,
DC, United States, November 2002. IEEE Computer Society.
[16] George W. Furnas, Louis M. Gomez, Thomas K. Landauer, and Susan T. Dumais.
Statistical semantics: How can a computer use what people name things to guess
what things people mean when they name things? In Proceedings of the 1982
Conference on Human Factors in Computing Systems, pages 251–253, New York,
NY, United States, March 1982. ACM Press.
[17] Rajiv Gandhi, Girish Kumar, Ben Bederson, and Ben Shneiderman. Domain name
based visualization of web histories in a zoomable user interface. In DEXA ’00:
Proceedings of the Eleventh International Workshop on Database and Expert Systems
Applications, pages 591–600, Washington, DC, United States, September
2000. IEEE Computer Society.
[18] Carl Gutwin. Focus and context: Improving focus targeting in interactive fisheye
views. In CHI ’02: Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, volume 4, pages 267–274, New York, NY, United States, April
2002. ACM Press.
[19] Ivan Herman, Guy Melan¸con, and M. Scott Marshall. Graph visualization and
navigation in information visualization: A survey. IEEE Transactions on Visualization
and Computer Graphics, 6(1):24–43, January 2000.
[20] Ron R. Hightower, Laura T. Ring, Jonathan I. Helfman, Benjamin B. Bederson,
and James D. Hollan. Graphical multiscale web histories: A study of PadPrints. In
HYPERTEXT ’98: Proceedings of the Ninth ACM Conference on Hypertext and
Hypermedia : Links, Objects, Time and Space—Structure in Hypermedia Systems,
pages 58–65, New York, NY, United States, June 1998. ACM Press.
[21] Mao Lin Huang. Information visualization of attributed relational data. In CRPITS
’01: Australian symposium on Information visualisation, pages 143–149,
Darlinghurst, Australia, Australia, 2001. Australian Computer Society, Inc.
[22] Eero Hyv¨onen, Miikka Junnila, Suvi Kettula, Eetu M¨akel¨a, Samppa Saarela, Mirva
Salminen, Ahti Syreeni, Arttu Valo, and Kim Viljanen. Metadata and beyond:
Finnish museums on the semantic web: The user’s perspective on MuseumFinland.
In Proceedings of Museums and the Web 2004 (MW2004), March/April 2004.
[23] Eero Hyv¨onen, Samppa Saarela, and Kim Viljanen. Ontogator: Combining view
and ontology-based search with semantic browsing. In Proceedings of the XML
Finland 2003 Conference, Open Standards, XML, and the Public Sector, October
2003.
[24] Inc. Inxight Software. Inxight startree. Retrieved June 2006 from
http://www.inxight.com.
[25] Brian Johnson and Ben Shneiderman. Tree-maps: A space-filling approach to the
visualization of hierarchical information structures. In VIS ’91: Proceedings of the
second Conference on Visualization ’91, pages 284–291, Los Alamitos, CA, United
States, October 1991. IEEE Computer Society Press.
[26] Susanne Jul and George W. Furnas. Navigation in electronic worlds: A chi 97
workshop. ACM SIGCHI Bulletin, 29(4):44–49, October 1997.
[27] Eser Kandogan. Visualizing multi-dimensional clusters, trends, and outliers using
star coordinates. In KDD ’01: Proceedings of the seventh ACM SIGKDD international
conference on Knowledge discovery and data mining, pages 107–116, New
York, NY, United States, 2001. ACM Press.
[28] Kirk L. Kroeker. Seeing data: New methods for understanding information. IEEE
Computer Graphics and Applications, 24(3):6–12, May/June 2004.
[29] John Lamping, Ramana Rao, and Peter Pirolli. A focus+context technique based
on hyperbolic geometry for visualizing large hierarchies. In CHI ’95: Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems, pages 401–408,
New York, NY, United States, May 1995. ACM Press/Addison-Wesley Publishing
Co.
[30] Tom Lanning, Kent Wittenburg, Michael Heinrichs, Christina Fyock, and Glenn
Li. Multidimensional information visualization through sliding rods. In AVI ’00:
Proceedings of the working conference on Advanced visual interfaces, pages 173–
180, New York, NY, United States, May 2000. ACM Press.
[31] Bongshin Lee, Cynthia Sims Parr, Dana Campbell, and Benjamin B. Bederson.
How users interact with biodiversity information using TaxonTree. In AVI ’04:
Proceedings of the Working Conference on Advanced Visual Interfaces, pages 320–
327, New York, NY, United States, May 2004. ACM Press.
[32] Jock Mackinlay. Automating the design of graphical presentations of relational
information. ACM Transactions on Graphics (TOG), 5(2):110–141, 1986.
[33] Eetu M¨akel¨a, Eero Hyv¨onen, Samppa Saarela, and Kim Viljanen. Ontoviews - a
tool for creating semantic web portals. In Proceedings of the Third International
Semantic Web Conference (ISWC2004), Lecture Notes in Computer Science, pages
797–811. Springer, November 2004.
[34] Eetu M¨akel¨a, Eero Hyv¨onen, and Teemu Sidoroff. View-based user interfaces for
information retrieval on the semantic web. In Proceedings of the Fourth International
Semantic Web Conference Workshop End User Semantic Web Interaction,
November 2005.
[35] Eetu M¨akel¨a, Kim Viljanen, Petri Lindgren, Mikko Laukkanen, and Eero Hyv¨onen.
Semantic yellow page service discovery: The veturi portal. In Poster paper of the
Fourth International Semantic Web Conference, November 2005.
[36] Gary Marchionini. Information Seeking in Electronic Environments. Cambridge
University Press, New York, NY, United States, 1995.
[37] Gary Marchionini and Ben Brunk. Towards a general relation browser: A GUI for
information architects. Journal of Digital Information, 4(1), April 2003.
[38] Gary Marchionini and Ben Shneiderman. Finding facts vs. browsing knowledge in
hypertext systems. Computer, 21(1):70–80, January 1988.
[39] Michael J. McGuffin, Gord Davison, and Ravin Balakrishnan. Expand-ahead: A
space-filling strategy for browsing trees. In INFOVIS ’04: Proceedings of the IEEE
Symposium on Information Visualization (INFOVIS’04), pages 119–126, Washington,
DC, United States, October 2004. IEEE Computer Society.
[40] Chris McMahon, Rose Crossland, Alistair Lowe, Tulan Shah, Jon Sims Williams,
and Steve Culley. No zero match browsing of hierarchically categorized information
entities. Artificial Intelligence for Engineering Design, Analysis and Manufacturing,
16(3):243–257, June 2002.
[41] Tamara Munzner. Exploring large graphs in 3D hyperbolic space. IEEE Computer
Graphics and Applications, 18(4):18–23, July/August 1998.
[42] MuseumFinland. http://museosuomi.cs.helsinki.fi/. Retrieved June 2006 from
http://museosuomi.cs.helsinki.fi/.
[43] David A. Nation, Catherine Plaisant, Gary Marchionini, and Anita Komlodi. Visualizing
websites using a hierarchical table of contents browser: WebTOC. In
Proceedings of the Third Conference on Human Factors and the Web, June 1997.
[44] Quang Vinh Nguyen and Mao Lin Huang. A space-optimized tree visualization. In
INFOVIS ’02: Proceedings of the IEEE Symposium on Information Visualization
(InfoVis’02), pages 85–92, Washington, DC, United States, October 2002. IEEE
Computer Society.
[45] Lucy Nowell, Robert Schulman, and Deborah Hix. Graphical encoding for information
visualization: An empirical study. In INFOVIS ’02: Proceedings of the IEEE
Symposium on Information Visualization (InfoVis’02), pages 43–50, Washington,
DC, United States, October 2002. IEEE Computer Society.
[46] Sung Park and Richard Catrambone. Represented and representing dimensions in
relational information displays. In 8th International Conference on Information
Visualisation (IV’04), pages 605–612, Washington, DC, United States, July 2004.
IEEE Computer Society.
[47] Catherine Plaisant, Jesse Grosjean, and Benjamin B. Bederson. SpaceTree: Supporting
exploration in large node link tree, design evolution and empirical evaluation.
In INFOVIS ’02: Proceedings of the IEEE Symposium on Information
Visualization (InfoVis’02), pages 57–64, Washington, DC, United States, October
2002. IEEE Computer Society.
[48] Catherine Plaisant, Ben Shneiderman, Khoa Doan, and Tom Bruns. Interface
and data architecture for query preview in networked information systems. ACM
Transactions on Information Systems, 17(3):320–341, July 1999.
[49] Haystack Project. The universal information client. Retrieved June 2006 from
http://haystack.lcs.mit.edu/.
[50] George G. Robertson, Jock D. Mackinlay, and Stuart K. Card. Cone trees: Animated
3D visualizations of hierarchical information. In CHI ’91: Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems, pages 189–194,
New York, NY, United States, April/May 1991. ACM Press.
[51] Doug Schaffer, Zhengping Zuo, Saul Greenberg, Lyn Bartram, John Dill, Shelli
Dubs, and Mark Roseman. Navigating hierarchically clustered networks through fisheye and full-zoom methods. ACM Transactions on Computer-Human Interaction,
3(2):162–188, June 1996.
[52] SearchTools.com. Faceted metadata search and browse. Retrieved June 2006 from
http://www.searchtools.com/info/faceted-metadata.html.
[53] Ben Shneiderman, David Feldman, Anne Rose, and Xavier Ferr´e Grau. Visualizing
digital library search results with categorical and hierarchical axes. In DL ’00:
Proceedings of the Fifth ACM Conference on Digital Libraries, pages 57–66, New
York, NY, United States, 2000. ACM Press.
[54] Vineet Sinha and David R. Karger. Magnet: Supporting navigation in semistructured
data environments. In SIGMOD ’05: Proceedings of the 2005 ACM SIGMOD
International Conference on Management of Data, pages 97–106, New York, NY,
United States, June 2005. ACM Press.
[55] Stanley S. Stevens. On the theory of scales of measurement. Science, New Series,
103(2684):677–680, 1946.
[56] Thinkmap. http://www.thinkmap.com. Retrieved June 2006 from
http://www.thinkmap.com.
[57] John Tait Ting-Sheng Lai and Sharon McDonald. Image browsing and navigation
using hierarchical classification. In CIR ’99: Proceedings of the Challenge of Image
Retrieval–the Second UK Conference on Image Retrieval, February 1999.
[58] Janet Wesson, MC du Plessis, and Craig Oosthuizen. A ZoomTree interface for
searching genealogical information. In AFRIGRAPH ’04: Proceedings of the third
International Conference on Computer Graphics, Virtual Reality, Visualisation
and Interaction in Africa, pages 131–136, New York, NY, United States, November
2004. ACM Press.
[59] Leland Wilkinson. The grammar of graphics, Second edition. Springer-Verlag New
York, Inc., New York, NY, United States, 2005.
[60] Kent Wittenburg, Tom Lanning, Michael Heinrichs, and Michael Stanton. Parallel
bargrams for consumer-based information exploration and choice. In UIST ’01:
Proceedings of the 14th annual ACM symposium on User interface software and
technology, pages 51–60, New York, NY, United States, 2001. ACM Press.
[61] Yahoo! http://www.yahoo.com. Retrieved January 2006 from
http://www.yahoo.com.
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