Multi-hierarchy Information Visualization Research Based on Three-Dimensional Display of Products System
Currently, the information on the Web is countless, which is throughout tens of thousands of Web sites all over the world. And the Web site intertwined with each other through hyperlinks between documents. Regardless of such a big scale of the Web information, it will continue expanding. How to access to the information on the Web easily has become a problem needed to be solved urgently. However, the way of accessing to the information is far from satisfactory. Information visualization will play an increasingly important role in helping people understand the structure of the information space, finding information needed quickly and preventing the lost in the information ocean effectively. The paper used the Multi-hierarchy information visualization on a specific e-commerce web site, and established a three-dimensional products display system. According to the analysis of users on business web site, the establishment of a representative user model was established. In accordance with the user model, system function was analyzed and integrated, and task analysis was hierarchical. Based on the user’s demand, the paper confirmed the content and the way of the showing. Finally the paper designed the system according to the information structure, interaction and information visualization.
Keywordsuser experience Information architecture visualization mapping Fuzzy Comprehensive Evaluation Method
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- 1.Carpendale, S., Light, J., Pattison, E.: Achieving higher magnification in context. In: Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology, Santa Fe (2004)Google Scholar
- 2.Chen, C.: Searching for intellectual turning points: Progressive Knowledge Domain Visualization. Proc. Natl.Acad. Sci. USA 101(suppl.) (2004)Google Scholar
- 3.Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia (2006)Google Scholar
- 4.Klinkenberg, R., Renz, I.: Adaptive information filtering:learning in the presence of concept drifts. In: Learning for Text Categorization, pp. 33–40. AAAI Press, Menlo Park (2003)Google Scholar
- 5.Morinaga, S., Yamanishi, K.: Tracking dynamics of topic trends using a finite mixture model. In: KDD 2004, Seattle, Washington, pp. 811–816. ACM, New York (2007)Google Scholar
- 6.Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks, arXiv: cond- mat/ 0309488 v1 (2003)Google Scholar
- 7.Tabah, A.N.: Literature dynamics: studies on growth, diffusion, and epidemics. Annual Review of Information Science and Technology 34, 249–286 (1999)Google Scholar