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ISBN: 978-972-8939-25-0 © 2010 IADIS6. CONCLUSIONThis article has presented a system to manage trails and its application in the deal opportunities discovery inubiquitous commerce environments. TrailM initially has been integrated with UbiTrade, but its proposal isgeneric and can be used by other systems. The use of trails allows the systems to be more effective, becausethey can use more precise information of the users’ behavior. The objective of the case study was todemonstrate that is viable to integrate ubiquitous systems with TrailM as well as to use the trails to improveautomatic decisions, in our case, deal opportunities.The main conclusions were: (1) the use of trails confirms the mobile computing potential to improve thedeal opportunity discovery; (2) the availability of the history of visited contexts stimulates the use of mobiledevices as instruments to access contextualized contents; (3) TrailM supports content distribution guided bytrails; (4) the prototype and the experiment attest the proposal feasibility.TrailM as well as its integration with UbiTrade is an initial proposal. The following activities will allowthe continuity of the study: (1) we will run additional tests with the trails obtained from simulation, forexample, the PreferredDealingContexts and TypeOfBusinessByRegion services were not used yet; (2) theheuristic to determine the preferred contexts (frequency of visit) can be improved through additionalinformation, for example, time of each use; (3) the services performance involving a significant amount ofrecords must be evaluated, because the experiment considered a few number.REFERENCESBaldauf, M., Dustdar, S. , and Rosenberg, F., 2007. A Survey on Context-aware Systems. International Journal of AdHoc and Ubiquitous Computing, Vol. 2, No. 4, pp. 263-277.Dey, A. et al, 2010. Location-Based Services. IEEE Pervasive Computing, Vol. 9, No. 1, pp. 11-12.Dey, A., Salber, D. , and Abowd, G., 2001. A conceptual framework and a toolkit for supporting the rapid prototyping ofcontext-aware application. Human-Computer Interaction, Vol. 16, No. 2, pp. 97-166.Diaz, A., Merino, P. , and Rivas, F.J., 2010. Mobile Application Profiling for Connected Mobile Devices. IEEEPervasive Computing, Vol. 9, No. 1, pp. 54-61.Driver, C. and Clarke, S., 2008. An application framework for mobile, context-aware trails. Pervasive and MobileComputing, Vol. 4, No. 5, pp. 719-736.Franco, L. et al, 2009. Exploring Business Opportunities in Ubiquitous Environments. VIII IADIS InternationalConference WWW/Internet. Roma, Italy, pp. 469-476.Hightower, J. and Borriello, G., 2001. Location Systems for Ubiquitous Computing. Computer, Vol. 34, No. 8, pp. 57-66.Hightower, J., LaMarca, A. , and Smith, I., 2006. Practical Lessons from Place Lab. IEEE Pervasive Computing, Vol. 5,No. 3, pp. 32-39.Hoareau, C. and Satoh, I., 2009. Modeling and Processing Information for Context-Aware Computing - A Survey. NewGeneration Computing, Vol. 27, No. 3, pp. 177-196.Levene, M. and Peterson, D., 2002. Trail Record and Ampliative Learning. London: University of London.Roussos, G. and Moussouri, T., 2004. Consumer perceptions of privacy, security and trust in ubiquitous commerce.Personal and Ubiquitous Computing, Vol. 8, No. 6, pp. 416-429.Satyanarayanan, M., 2001. Pervasive computing: vision an challenges. IEEE Personal Communications, Vol. 8, No. 4,pp. 10-17.Satyanarayanan, M. et al, 2009. The Case for VM-Based Cloudlets in Mobile Computing. IEEE Pervasive Computing,Vol. 8, No. 4, pp. 14-23.Shekar, S., Nair, P. , and Helal, S., 2003. iGrocer: a ubiquitous and pervasive smart grocery. Symposium on AppliedComputing. Melbourne, USA, pp. 645-652.Smith, A.D. et al, 2006. Towards Truly Ubiquitous Life Annotation. Memories for Life Colloquium. London, pp. 1-2.Vaughan-Nichols, S.J., 2009. Will Mobile Computing's Future Be Location? Computer, Vol. 42, No. 2, pp. 14-17.Zhang, L., Liu, Q. , and Li, a.X., 2009. Ubiquitous Commerce: Theories, Technologies, and Applications. Journal ofNetworks, Vol. 4, No. 4, pp. 271-278.26