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Autor
Chu Sydney C. K., Ho James K., Lam S.S.
Tytuł
Optimized Star Plot as Decision Aids: Applications of Maximum Resolution Topology.
Źródło
Multiple Criteria Decision Making / University of Economics in Katowice, 2009, vol. 4, s. 41-59, bibliogr.26 poz.
Słowa kluczowe
Podejmowanie decyzji, Systemy wspomagania decyzji, Wizualizacja danych, Modele optymalizacyjne
Decision making, Decision Support Systems (DSS), Data visualisation, Optimizing models
Uwagi
summ., Korespondencja z redakcją: numeracja wpisana za zgodą redakcji (wynika z ciągłości wydawniczej serii MCDM) - brak numeracji na stronie tytułowej
Abstrakt
The traditional star plot has been a longstanding means of presenting multivariate data. Its early-days use can be traced back to "star symbol plot" of automobile data for large rays to represent favorable characteristics. Another pioneering use in clinical data is a graphical way of summarizing patient's evolving responses. Since the early 1970s and with the coined name of Kiviat plot or graph, wide-spread use in visualizing computer and program performance has become industrial standard among software engineers. It is ever more so in the modern advance of computer graphics, transformed into popular evaluation tools such as 2/3-D Kiviat graph and 3-D Kiviat tube. Its importance amidst forward technological strides remains largely in its ease of visualization, qualitatively on the basis of the shape of a star plot. In recent years we have staged a series of studies, by focusing of its analysis and topology, resulting in usefulness in the following extensions. First, a (canonical) star plot topology for high-dimensional data visualization is applied to data records of, specifically, multi-attribute dichotomies. Our project on data analysis of on-line auction markets provides such generic sample usage for dimensions identified in constructing a multi-attribute dichotomy to help discern relative empirical advantages to buyers and sellers. The second stage, of data and optimization modeling aspects, bases on the deeper observation that the areas of the plot for the two parts of a dichotomy may be used quantitatively as an aggregate measure of their relative dominance. An optimization GP model is developed to determine a topology ? the geometry and the arrangement of dimensions ? that maximizes the resolution of this measure with respect to a given set of reference dichotomies. The outcome of this modeling phase is what we call an MRT (or Maximum Resolution Topology), that in the sense of maximally discriminating its dichotomy of a set of multi-attribute data records, it is an overall best representation (accompanied by an "optimized" visualization). The third stage is the coding of MRT construction integrated into a spreadsheetstyle decision support system (MRT-DSS). Its ease of use has been promising and robust for diverse applications. Samples of these will conclude the paper as illustrations (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
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Bibliografia
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  2. Chu S.C.K., Ho J.K.: Topological and Optimization Modeling for Internet Data of Online Auction Markets. Proceedings of ITI2005 (27th International Conference on Information Technology Interfaces). Cavtat, Croatia June 20-23, 2005, 5.
  3. Chu S.C.K., Ho J.K., Lam S.S.: Optimization Modeling DSS for Maximum Resolution Topology of Internet Data. Institute of Industrial Engineering Annual Conference. CD-Rom Proceedings. Orlando, FL May 20-24, 2006, 6.
  4. Friedman H.P., Farrell E.J., Goldwyn R.M., Miller M., Siegel J.H.: A Graphical Way of Describing Changing Multivariate Patterns. Proceedings of the Computer Science and Statistics 6th Annual Symposium on the Interface. University of California, Berkeley October 16-17, 1972, pp. 56-59.
  5. Hackstadt S.T. and Malony A.D.: Visualization Parallel Programs and Performance. "IEEE Computer Graphs and Applications" 1995, Vol. 15(4), pp. 12- -14.
  6. Heath M.T. and Etheridge J.A.: Visualizing the Performance of Parallel Programs. "IEEE Software" 1991, Vol. 8(5), pp. 29-39.
  7. Heath M.T., Malony A.D., Rover D.T.: The Visual Display of Parallel Performance Data. "Computer" 1995, Vol. 28(11), pp. 21-28.
  8. Ho J.K.: Topological Analysis of Online Auction Markets. "International Journal of Electronic Markets" 2004, Vol. 14(3), pp. 202-213.
  9. Ho J.K.: Topology of Online Auction Markets. In: Competitive Bidding and Auctions II. K.K. Lai et al. (eds). Global-Link Publisher, Hong Kong 2004, pp. 1-9.
  10. Ho J.K.: A Global Comparative Study of Online Auction Markets. In: Shaping Business Strategy in a Networked World. J. Chen (ed.). Proceedings of ICEB2004, Beijing, China December 5-9, 2004, pp. 239-244.
  11. Ho J.K.: Maximum Resolution Dichotomy for Global Diffusion of the Internet. "Communications of the Association of Information Systems" 2005, Vol. 16, pp. 797-809.
  12. Ho J.K.: Maximum Resolution Dichotomy for Investment Climate Indicators. "International Journal of Business Environment" 2006, Vol. 1(1), pp. 126-135
  13. Ho J.K.: Maximum Resolution Dichotomy for Customer Relations Management. In Data Mining VII. A. Zanansi, C. Brebbia, and N. Ebecken (eds). WIT Press, Southampton 2006, pp. 279-288.
  14. Ho J.K.: Online Auction Markets in Tourism. "Information Technology and Tourism" 2008, Vol. 10(1), pp. 19-29.
  15. Ho J.K.: Inter-Brand Comparison of Online Auction Markets. "Electronic Commerce Research" 2008, Vol. 8, pp. 103-114.
  16. Ho J.K., Chu S.C.K.: Maximum Resolution Topology for Multi-attribute Dichotomies. "Informatica" 2005, Vol. 16(4): pp. 557-570.
  17. Ho J.K., Chu S.C.K., Lam S.S.: Maximum Resolution Topology for Online Auction Markets. "International Journal of Electronic Markets" 2007, Vol. 17(2), pp. 164-174.
  18. Ho J.K., Chu S.C.K., Lam S.S.: Optimization Model and DSS for Maximum Resolution Dichotomies. In: ICINCO 2007, Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization. J. Zaytoon, J.-L. Ferrier, J. Andrade- Cetto, J. Filipe (eds). Angers, France May 9-12, 2007, pp 355-358.
  19. Kolence K.W.: The Software Empiricist. "ACM SIGMETRICS Performance Evaluation Review" 1973, Vol. 2(2), pp. 31-36.
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  21. Morris M.F.: Kiviat Graphs: Conventions and Figures of Merit. "ACM SIGMETRICS Performance Evaluation Review" 1974, Vol. 3(3), pp. 2-8.
  22. Muller W., Schumann H.: Visualization Methods for Time-Dependent Data: An Overview. Proceedings of 2003 Winter Simulation Conference. Vol. 1. New Orleans Dec 7-10, 2003, pp. 737-745.
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  24. Scniederjans M.J.: Goal Programming Methodology and Applications 1995. Kluwer Publishers, Boston 1995.
  25. Spence R.: Information Visualization 2001. Pearson Education Ltd., ACM Press, Harlow.
  26. Wever H.E.: Petroleum and Source Rock Characterization Based on C7 Star Plot Results: Examples from Egypt. "American Association of Petroleum Geologists (AAPG)" 2000, Vol. 84(7), pp.1041-1054.
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ISSN
2084-1531
Język
eng
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