彈性資訊視覺化方法之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：56

、訪客IP：3.149.233.32

姓名

巫亮宏(Liang-Hong Wu) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

彈性資訊視覺化方法之研究
(A Flexible Information VisualizationMethod Based on User Perception)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

由於企業管理已進入了電子化的時代，因此企業的電子資料量以指數
型態不斷的增加著。如何在如此大量的電子資料中順利的進行資料挖掘變成一件有難度的工作。而資訊視覺化方法可以把這些大量而複雜的電子資料以不同的圖形形式呈現出來，並巧妙的利用了人類天生優於電腦的對於圖形的感知能力，因此克服了對於大量而複雜企業電子資料的分析障礙。
但是傳統的資訊視覺化方法僅僅提供了二維的操控能力，而觀察資料的角度也是固定且有限的，因此所有的使用者皆受限在固定的操控模式與觀察角度之下。而Magic Eye View 提出了一個新的三維操控、多角度觀察的資訊視覺化方法。讓不同的使用者可以根據自己的需要去操控、去選擇自己的角度去感知它們所選擇之資料。但是，Magic Eye View 在由二維操控進化成為三維操控的過程當中，把某些傳統資訊視覺化方法的優良特性遺漏了。因此本研究提出了新的資訊視覺化方法，不但由二維操控進化成三維操控、提供了多角度觀察能力，而且將Magic Eye View 所遺漏的傳統資訊視覺化方法之特性補足。成為了一個具有彈性能夠符合不同使用者需求的、功能完整的三維操控資訊視覺化方法。

摘要(英)

As the amount data stored in digital format continues to grow at a rapid pace, exploring the relationships within such data is becoming increasingly difficult.
Information visualization methods use the human perceptual system to help users analyze the complex relationships, and graphical hierarchy trees are usually employed to present the relationships. Conventional methods fail to consider human factors and only provide a fixed degree of detail to different users, but users have different perceptions. Magic Eye View, a well-known information visualization tool, uses a three-dimensional interaction that allows
the users to control the degree of detail they would like. However, it neglects some crucial focus + context features. To address this problem, we propose two novel information visualization methods. It not only enables users to control the focus points three-dimensionally and view the preferred degree of detail in the information space, but also provides crucial focus + context features that help users understand the data based on their own knowledge and perceptions. The results of simulations show that the proposed model improves on the performance of Magic Eye View by incorporating those crucial features.

關鍵字(中)

★ 三維操控
★ 感知
★ 資訊視覺化

關鍵字(英)

★ 3D control
★ Perception
★ Information Visualization

論文目次

Contents
List of Figures ...................................................................................................... v
List of Tables...................................................................................................... vii
Chapter 1. Introduction ...................................................................................... 1
Chapter 2. Literature Review ............................................................................ 6
Chapter 3. The Crystal Ball View .................................................................... 13
3.1 The Model of the Crystal Ball View ............................................................. 13
3.2 The Information Visualization of the Crystal Ball View ......................... 19
3.3 Intuitively Contorl of the Crystal Ball View ............................................... 22
3.4 The Selection of the Focus Region of the Crystal Ball View ................. 24
Chapter 4. The Advanced Perceptual Eye View ............................................ 26
4.1 The Mathematical Model of the Advanced Perceptual Eye View ......... 28
4.2 The Advanced Perceptual Can Fully Utilize the Screen Space .............. 32
4.3 The Advanced Perceptual Eye View Maintain the Whole Global
Context at All Times .............................................................................................. 35
4.4 Intuitively Control of the Advanced Perceptual Eye View ..................... 38
4.5 The Selection of the Focus Region of the Advanced Perceptual
Eye View ................................................................................................................... 40
Chapter 5. Conclusion and Future Work ....................................................... 47
5.1 Future Work of Crystal Ball View ................................................................. 48
5.2 Future Work of Advanced Perceptual Eye View ....................................... 50
References .......................................................................................................... 51

參考文獻

[1] D. A. Keim. “Information visualization and visual data mining.” IEEE
Transactions on Visualization and Computer Graphics, 8(1):1-8, 2002.
[2] J. Foley. “Getting there: The ten top problems left.” IEEE Computer
Graphics and Applications, 20(1):66-68, 2000.
[3] B.H. McCormick, T.A. DeFanti, and M.D. Brown. “Visualization in
scientific computing. Computer Graphics, 21(6):1345-1366, 1987.
[4] M. Tory and T. Moller. “Human factors in visualization research.” IEEE
Transactions on Visualization and Computer Graphics, 10(1):1-13, 2004.
[5] M.O. Ward. “A taxonomy of glyph placement strategies for
multidimensional data visualization.” Information Visualization, 1:194-210,
2002.
[6] E. Sharlin, B. Watson, S. Sutphen, L. Liu, R. Ledere, and J. Frazer. “A
tangible user interface for assessing cognitive mapping ability.”
International Journal of Human-Computer Studies, 67(3):269-278, 2009.
[7] G.S. Hubona, G.W. Shirah, and D.G. Fout. “The effects of motion and
stereopsis on three-dimensional visualization.” International Journal of
52
Human-Computer Studies, 47(5):609-627, 1997.
[8] G.J. Wills. “Nicheworks- interactive visualization of very large graphs.”
Journal of Computational and Graphical Statistics, 8(2):190-212, 1999.
[9] U. Dogrusoz, E. Giral, A. Cetintas, A. Civril, and E. Demir. “A layout
algorithm for undirected compound graphs.” Information Sciences,
179:980-994, 2009.
[10] U. Dogrusoz, K.G. Kakoulis, B. Madden, and I.G. Tollis. “On labeling in
graph visualization.” Information Sciences, 177:2459-2472, 2009.
[11] O. Gilson, N. Silva, P.W. Grant, and M. Chen. “From web data to
visualization via ontology mapping.” Computer Graphics Forum,
27(3):959-966, 2008.
[12] C. Ware and G. Franck. “Evaluating stereo and motion cues for visualizing
information nets in three dimensions.” ACM Transactions on Graphics,
15:121-139, 1996.
[13] C. Collins and S. Carpendale. “Vislink: Revealing relationships amongst
visualizations.” IEEE Transactions on Visualization and Computer
Graphics, 13(6):1192-1199, 2007.
[14] S. Card, J. Mackinlay, and B. Shneiderman. Readings in Information
53
Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc.,
1999.
[15] N. Gershon, S.G. Eick, and S. Card. “Information visualization.”
Interactions, 5(2):9-15, 1998.
[16] M. Sarkar, S.S. Snibbe, O.J. Tversky, and S.P. Reiss. “A metaphor for
viewing large layouts on small screens.” In Proceedings of the 6th Annual
ACM Symposium on User Interface Software and Technology, pages
81-91, 1993.
[17] J. Han, X. Hu, and N. Cercone. “A visualization model of interactive
knowledge discovery systems and its implementations.” Information
Visualization, 2:105-125, 2003.
[18] M. Dastani. “The role of visual perception in data visualization.” Journal of
Visual Languages and Computing, 13:601-622, 2002.
[19] C.G. Healey, S. Kocherlakota, V. Rao, R. Mehta, and R.S. Amant. “Visual
perception and mixed-initiative interaction for assisted visualization design.”
IEEE Transactions on Visualization and Computer Graphics,
14(2):396-411, 2008.
[20] D. Avrahami, D. Gergle, S.E. Hudson, and S. Kiesler. “Improving the
match between callers and receivers: A study on the effect of contextual
54
information on cell phone interruptions.” Behaviour and Information
Technology, 26(3):247-259, 2007.
[21] A. Serenko, N. Bontis, and B. Detlor. “End-user adoption of animated
interface agents in everyday work applications.” Behaviour and
Information Technology, 26(2):119-132, 2007.
[22] C. Ling, W. Hwang, and G. Salvendy. “A survey of what customers want in
a cell phone design.” Behaviour and Information Technology,
26(2):149-163, 2007.
[23] S. Moser, P. Degener, R. Wahl, and R. Klein. “Context aware terrain
visualization for wayfinding and navigation.” Computer Graphics Forum,
27(7):1853-1860, 2008.
[24] D. Schaffer, Z. Zuo, S. Greenberg, L. Bartram, J. Dill, S. Dubs, and M.
Roseman. “Navigating hierarchically clustered networks through fisheye
and full-zoom methods.” ACM Transactions on Computer Human
Interaction, 3(2):162-188, 1996.
[25] J. S. Yi, Y. a. Kang, J. T. Stasko, and Jacko J.A. “Toward a deeper
understanding of the role of interaction in information visualization.” IEEE
Transactions on Visualization and Computer Graphics, 13(6):1224-1231,
2007.
55
[26] L. Kjelldahl and B.N. Schenkman. “Colour induction on computer displays
- adjacency and shape effects.” Behaviour and Information Technology,
26(3):261-272, 2007.
[27] J. Lamping and R. Rao. “The hyperbolic browser: A focus + context
technique for visualizing large hierarchies.” Journal of Visual Languages
and Computing, 7(1):33-55, 1996.
[28] C. Gutwin. “Improving focus targeting in interactive fisheye views.” In
Proc. CHI 02, volume 4, pages 267-274, 2002.
[29] M. Kreuseler and H. Schumann. “A flexible approach for visual data
mining.” IEEE Transactions on Visualization and Computer Graphics,
8(1):39-51, 2002.
[30] Liang-Hong Wu and Ping-Yu. Hsu. “The perceptual eye view: A
user-defined method for information visualization.” Lecture Notes in
Computer Science, 4551:181-190, 2007.
[31] M.S.T. Carpendale, D.J. Cowperthwaite, and F.D. Fracchia.
“Threedimensional pliable surfaces: For effective presentation of visual
information.” In Proceedings of the 8th annual ACM Symposium on User
Interface and Software Technology, pages 217-226, 1995.
[32] M. Sarkar and M.H. Brown. “Graphical fisheye views.” Communications
56
of the ACM, 37(12):73-84, 1994.
[33] G.W. Furnas. “Generalized fisheye views.” In Proceedings of the ACM
SIGCHI Conference on Human Factors in Computing Systems, pages
16-23, 1986.
[34] J. Lamping, R. Rao, and P. Pirolli. “A focus + context technique based on
hyperbolic geometry for visualizing large hierarchies.” In Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems, pages
401-408, 1995.
[35] T. Munzner. “Exploring large graphs in 3d hyperbolic space.” IEEE
Computer Graphics and Applications, 18(4):18-23, 1998.
[36] D. G. Purcell and A. L. Stewart. “The object detection effect: Configuration
enhances perception.” Perception and Psychophysics, 50(3):215-224, 1991.
[37] J. Bottger, M. Preiser, M. Balzer, and O. Deussen. “Detail-in-context
visualization for satellite imagery.” Computer Graphics Forum,
27(2):587-596, 2008.
[38] L.E. Thomas and A. Lleras. “Swing into thought: Directed movement
guides insight in problem solving.” Psychonomic Bulletin & Review, 2009.
[39] E. Hornecker and A. Dunser. “Of pages and paddles: Childrens
57
expectations and mistaken interactions with physicalvdigital tools.”
Interacting with Computers, 21:95-107, 2009.
[40] A. Khatchatourov, J. Castet, J. Florens, A. Luciani, and C Lenay.
“Integrating tactile and force feedback for highly dynamic tasks:
Technological, experimental and epistemological aspects.” Interacting with
Computers, 21:26-37, 2009.
[41] S. Sinclair and M.M. Wanderley. “A run-time programmale simulator to
enable multi-modal interaction with rigid-body systems.” Interacting with
Computers, 21:54-63, 2009.
[42] Y. Visell. “Tactile sensory substitution: Models for enaction in hci.”
Interacting with Computers, 21:38-53, 2009.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2010-1-6

推文