Personal and Ubiquitous Computing

, Volume 17, Issue 8, pp 1753–1760 | Cite as

Social relation-based dynamic team organization by context-aware matchmaking

  • Keonsoo Lee
  • Jaehoon Kim
  • Seungmin Rho
  • Hangbae Chang
Original Article

Abstract

Team is a group of people who share the common object. However, in the dynamically changing situation, the existing members may be excluded, and new members may be included. This change is one of the most critical problems, which cause the failure of the team. In this paper, we propose a way of maintaining team in spite of the member’s modification. In order to achieve this object, we employ an abstract layer between team and member. This layer provides roles to the team by hiding the members. Therefore, a team can be free from the actual members, and members can be free from binding to a specific team. The roles binding, which connects member to the team, is provided in the proposed model according to the situation by matchmaking the best suitable member for a given role. By this, even the team’s members are changed, the role, which is required in the team, can be bound to other suitable member. In the matchmaking process, it is not sufficient to find a member, who has ability to perform the required role, because a team needs a fellowship among the members. For this, a social relation is used as criteria for calculating the suitability of role binding.

Keywords

Dynamic team organization Role binding Social relation 

References

  1. 1.
    James P. Lewis (1997) Team-based project management. AMACOM ISBN-10: 0814403646Google Scholar
  2. 2.
    WordNet 3.0 Princeton University. Wordnetweb.princeton.edu
  3. 3.
    Jacobson I, Christerson M, Jonsson P, Overgaard G (1996) Object-oriented software engineering—a use case driven approach. Addison-Wesley ISBN-10: 0201544350Google Scholar
  4. 4.
    Kendall E (1998) Agent roles and role models: new abstraction for MAS analysis and design. In: Intelligent agents in information and process, Workshop at the 22nd German annual conference on artificial intelligence pp 35–46Google Scholar
  5. 5.
    Becht M, Gurzki T, Klarmann J, Muscholl M (1999) ROPE: role oriented programming environment for multiagent systems. In: The fourth IFCIS conference on cooperative information systems (CoopIS’99)Google Scholar
  6. 6.
    Nwana HS (1996) Software agents: an overview. Knowl Eng Rev 11(3):205–244CrossRefGoogle Scholar
  7. 7.
    Wooldridge M, Jennings N (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10(2):115–152CrossRefGoogle Scholar
  8. 8.
    Wooldridge M, Jennings NR, Kinny D (2000) The gaia methodology for agent-oriented analysis and design. J Auton Agent Multi Agent Syst 3(3):285–312CrossRefGoogle Scholar
  9. 9.
    Ranganathan A, Chetan S, Al-Muhtadi J, Campbell R, Mickunas M (2005) Olympus: a high-level programming model for pervasive computing environment. In: Proceedings IEEE PerCom pp 7–16Google Scholar
  10. 10.
    Roman M, Hess C, Cerqueira R, Ranganathan A, Campbell R, Nahrstedt K (2002) A middleware infrastructure for active spaces. IEEE Pervasive Comput 1(4):74–83CrossRefGoogle Scholar
  11. 11.
    Schilit BN, Adams N, Want R (1994) Context-aware computing applications. In: Proceedings of the workshop on mobile computing systems and applications, pp 85–90Google Scholar
  12. 12.
    Dey A, Abowd G (2000) Towards a better understanding of context and context-awareness. In: Proceedings of the CHI workshop on the what, who, where, when, and how of context-awarenessGoogle Scholar
  13. 13.
    http://support.microsoft.com/kb/245115 Using early binding and late binding in Automation
  14. 14.
    Sycara K, Klusch M, Widoff S (1999) Dynamic service matchmaking among agents in open information environments. SIGMOD Rec 28(1):47–53CrossRefGoogle Scholar
  15. 15.
    Juokka D, Harada L (1995) Matchmaking for information agents. In: Proceedings of 14th IJCAI, pp 672–678Google Scholar
  16. 16.
    Good N, Schafer B, Konstan J, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of AAAI-99 conference, pp 439–446Google Scholar
  17. 17.
    Schafer B, Konstan J, Riedl J (1999) Recommender systems in E-commerce. In: Proceedings of ACM E-commerce 1999 conferenceGoogle Scholar
  18. 18.
    Buchanan BG, Shortliffe EH (1984) Rule-based expert systems: the MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-WesleyGoogle Scholar
  19. 19.
    Brachman RJ, Khabaza T, Kloesgen W, Piatetsky-Shapiro G, Simoudis E (1996) Mining business database. Commun ACM 39(11):42–48CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Keonsoo Lee
    • 1
  • Jaehoon Kim
    • 1
  • Seungmin Rho
    • 2
  • Hangbae Chang
    • 3
  1. 1.Wisepole Research and Development DivisionIncline B/D Deachi-dongKangnam-gu, SeoulKorea
  2. 2.Division of Information and CommunicationBaekseok UniversityCheonan-cityKorea
  3. 3.Department of Business AdministrationSangmyung UniversitySeoulKorea

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