Principles of Information Visualization for Business Research
AbstractIn the era of data-centric-science, a large number of visualization tools have been created to help researchers understand increasingly rich business databases. Information visualization is a process of constructing a visual presentation of business quantitative data, especially prepared for managerial use. Interactive information visualization provide researchers with remarkable tools for discovery and innovation. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional spaces. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100 msec updates even for million-record databases. Visualizations of business information are therefore widely used in actually business decision support systems, and by business researchers also. Visual user interfaces called dashboards are tools for reporting the status of a company and its business environment to facilitate business intelligence and performance management activities. In this study, we examine the research on concepts, and the principles of business information visualization, because we hope to be using correctly by business Ph.D. students in their researches. Visual representations are likely to improve business managers, and business researchers efficiency, offer new insights, and encouraging comparisons.
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Bibliographic InfoArticle provided by Alexandru Ioan Cuza University, Faculty of Economics and Business Administration in its journal Review of Economic and Business Studies.
Volume (Year): (2008)
Issue (Month): 2 (November)
business quantitative data; visual presentation; visual interfaces; performance management; business intelligence;
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- Friendly, Michael & Kwan, Ernest, 2003. "Effect ordering for data displays," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 509-539, August.
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