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1、Information-Statistical Approach for a Strategic Planning of a Community-Based Wireless ProjectWei-Tsu Yang1 and Bon K. Sy21 Queens College/CUNY, Computer Science Department, Flushing NY 11367, U.S.A. weyangscils.rutgers.edu2 Queens College/CUNY, Computer Science Department, Flushing NY 11367, U.S.A
2、. bonbunny.cs.qc.eduAbstract. The objective of this paper is to apply an information-statistical data mining technique to achieve two specific goals related to building a community-based wireless infrastructure. The first goal is to discover from data the characteristics behind a successful project
3、on building a community-based wireless infrastructure; e.g., NYCwireless. The second goal is to estimate the node distribution of a wireless infrastructure if such a community-based wireless project is to be invested and expanded to the Queens county of New York City. The first step is to discover s
4、tatistically significant event patterns that attempt to explain the characteristics of the project - NYCwireless. Then an information-statistical approach is applied to discover an optimal probability model based on Shannon entropy criterion for estimating the node distribution of a projected wirele
5、ss infrastructure in Queens County of New York City. 1 IntroductionNYCwireless 1 is a non-profit organization that provides free wireless Internet service over radio connections to mobile users in public spaces such as coffee shops and parks throughout Manhattan metropolitan area in New York City. E
6、ach node is operated independently by volunteers using their own equipment. By the end of April 2002, over ninety network nodes were listed in the NYCwireless database for the New York City metropolitan area. More than half of them (49) are located at Manhattan. On the contrary, Queens County accoun
7、ts for less than 10% of the nodes. Yet from the population survey conducted by the U.S. Census Bureau 2, Queens County has a larger population size than Manhattan. In this paper, we attempt to answer two specific questions that may provide valuable information for strategic planning and to assist in
8、 the decision making process behind building a community-based wireless infrastructure in the Queens County of New York City: 1. What are the characteristics, quantified by statistical significant event patterns, of the active participants who contributed to the community-based wireless infrastructu
9、re in Manhattan? 2 Wei-Tsu Yang1 and Bon K. Sy22. Based on the information revealed by statistical significant event patterns, what is the optimal probability model, with respect to Shannon entropy criterion, that can be used as a basis for projecting the spatial distribution of wireless nodes in th
10、e Queens County of New York City if identical resources as that of NYCwireless invested in Manhattan are applied to Queens?2 Information Statistical ApproachSince the primary goal of our project is to discover the relationship between event patterns and the distribution of wireless nodes, informatio
11、n statistical approach is chosen. Information statistical approach fits very well for uncovering unknown event patterns that are statistically significant 3. The choice of representation and characteristics for capturing the behavior of wireless node owners is still an open issue. In this research,
12、we choose a representation framework that is based on multi- valued variables, and we apply an information statistical approach to reveal the information embedded in the event patterns. Information statistical approach for data mining is built upon the concept of patterns. Concept of patterns is com
13、mon in the data mining community 4. Grenander has discussed extensively a general concept of patterns from the perspective of applied mathematics with application to understanding the relationship between image set patterns and statistical geometry 5,6,7. One notion of the concept of patterns that w
14、e have explored is to capture the meaning and the quality of the information embedded in data. In comparison to the concept of patterns discussed by Grenander, one interesting aspect found by us is the possibility of interpreting joint events of discrete random variables surviving statistical hypoth
15、esis test of interdependency as statistically significant association patterns. In doing so, significant previous works already established 8,9,10,11,12,13 may be used to provide a unified framework for linking information theory with statistical analysis. The significance of such a linkage is that
16、it not only provides a basis for using statistical approaches for revealing hidden significant association patterns, but for using information theory as a measurement instrument to determine the quality of information obtained from statistical analysis. For further details on the application of statistical techniques for analyzing and discovering statistical patterns, and information theory for interpreting the meaning b
