Chiang Mai Journal of Science

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This paper is one of the first written that ties together the areas of wellness, social networking, and computational-complexity theory. Our goal is to integrate these three fields in order to develop a system in which we can help people improve their well being. There are over one billion obese people in the world and that number is growing alarmingly fast. We must find a way to motivate unhealthy people to take care of themselves, so as to put less strain on economies around the world, less strain on natural resources, less strain on the health-care system, less strain on nearby individuals, and, most importantly, to save lives and to reduce suffering. We have gone from hunters and gathers to sitters and twitterers. As our standard of living increases, so does our size. While eating high-caloric foods, many people watch TV or play with the Internet all day long. And one particular Internet application, social networking, is amazingly popular-nations around the world have a significant percentage of their populations engaged in social-networking sites such as Facebook, Flickr, Friendster, hi5, LinkedIn, MySpace, Orkut, Qzone, and so on. In this paper we leverage the popularity of social networking to improve people’s wellness. We propose a WELLNESS PROFILE MODEL that captures a social-network members’ characteristics, preferences, activities, vitals, and other relevant information. We formulate dynamic-matching problems whose focus is to find groups of individuals with similar interests and time constraints, so that they can participate in activities together thereby contributing to each other’s well being. We examine the computational complexity of the problems from both the sequential and parallel perspectives. Our results tell us how hard these problems are to solve. That is, we determine the complexity of these problems. The problems defined here involve a new type of dynamic matching, as opposed to traditional matching as is done in many of the current Internet social-networking sites and traditional graph theory. We intend to implement restricted versions of some of the algorithms in the systems being developed by Elbrys Networks, Inc. Keywords: algorithms, computational-complexity theory, dynamic matching, social networking, mobile wellness applications, wellness profile model.

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