Skip to main content
SpringerLink
Log in

Modeling service representatives in enterprise systems using generic agents

  • Original Research Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

As a common practice in business enterprise systems, a service provider delegates a human agent to a client site to serve the client. On the other hand, in a computerized business application, enterprise organizations adopt Service-Oriented Architecture (SOA), where an enterprise agent is modeled as a software agent that cannot be transmitted efficiently by service messages. In the proposed approach, we extend the traditional architecture of SOA implementations to support generic and lightweight agents that reside at the client site. These agents, that we call “Service representatives”, can be customized and trained based on the provider generated role description and knowledge to perform their assigned tasks. In addition to providing innovative applications, such a technique allows for more sophisticated features such as maintaining client privacy and separating the functionality of the service and its delegated agent. To indicate the variety of roles that can be done by the service representative, we provide three case studies to show how a local and generic agent can be customized by different providers to personalize financial advice, apply medical guidelines, and verify credit card transactions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Soja P, Paliwoda-Pekosz G (2009) What are real problems in enterprise system. Ind Manag Data Syst 109(5): 610–627

    Article  Google Scholar 

  2. Krafzig D, Banke K, Slama D (2005) Enterprise SOA: service oriented architecture best practices. Prentice-Hall, USA

    Google Scholar 

  3. Le D, Eck SA, Cao T (2007) A survey of web service discovery systems. Int J Inf Technol Web Eng 2(2): 65–80

    Article  Google Scholar 

  4. Hung PCK, Li H, Jeng J (2004) WS-Negotiation: an overview of research issues. In: The 17th annual Hawaii international conference on system sciences, Hawaii, pp 10–18

  5. Dustdar S, Schreiner W (2005) A survay on web service composition. Int J Web Grid Serv 1(1): 1–30

    Article  Google Scholar 

  6. Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2(4): 263–277

    Article  Google Scholar 

  7. Huebscher MC, McCann JA (2008) A survey of autonomic computing—degrees, models, and applications. ACM Comput Surv 40(3): 1–28

    Article  Google Scholar 

  8. Alessandrini M, Lippe W, Nuesser W (2008) Intelligent service system: an agent-based approach for integrating artifical intelligence components in SOA landscapes. In: International conference on web intelligence and intelligent agent technology, Sydney, pp 496–499

  9. Sycara K, Paolucci M, Soundry J, Srinivasan N (2004) Dynamic discovery and coordination of agent-based semantic web service. IEEE Internet Comput 8(3): 66–73

    Article  Google Scholar 

  10. Lee S, Choi K, Shin H, Shin D (2007) Ag Webs: web services based on intelligent agent platform. In: The 9th international conference on advanced communication technology, Phonex, Korea, pp 353–356

  11. Cheng Y, Leon-Garcia A, Foster I (2008) Toward an autonomic service management framework: a holistic vision of SOA, AON, and autonomic computing. IEEE Commun Mag 46(5): 138–146

    Article  Google Scholar 

  12. Xu B, Yang X, Shen Y, Shanping L, Ma A (2008) A role-based SOA architecture for community support systems. In: International symposium on collaborative technologies and systems, Irvine, pp 408–415

  13. Maamar Z, Mostefaoui S, Yahyaoui H (2005) Toward an agent-based and context-oriented approach for web services composition. IEEE Trans Knowl Data Eng 17(5): 686–697

    Article  Google Scholar 

  14. Yamato Y, Ohnishi H, Sunaga H (2008) Study of service processing agent for context-aware service coordination. In: IEEE international conference on service Computing, Hawaii, pp 275–282

  15. Xiang L (2007) A multi-agent-based service-oriented architecture for inter-enterprise cooperation system. In: Second international conference on digital telecommunications, Silicon Vally, pp 22–32

  16. Khalili A, Badrabadi A, Khoshalhan F (2008) A framework for distributed market place based on intelligent software agents and semantic web services. In: IEEE international congress on services part II, Hawaii, pp 141–148

  17. Wong D, Paciorek N, Walsh T, DiCelie J, Young M, Peet B (1997) Concordia: an infrastructure for collaborating mobile agents. In: MA ’97: proceedings of the first international workshop on mobile agents, Springer London, pp 86–97

  18. Wong D, Paciorek N, Moore D (1999) Java-based mobile agents. Commun ACM 42(3): 92–102

    Article  Google Scholar 

  19. Pham V, Karmouch A (1998) Mobile software agents: an overview. IEEE Commun Mag 36: 26–37

    Article  Google Scholar 

  20. Sangiorgi D, Walker D (2001) PI-Calculus: a theory of mobile processes. Cambridge University Press, New York

    MATH  Google Scholar 

  21. Zins C (2007) Knowledge map of information science. J Am Soc Inf Sci Technol 58(4): 526–535

    Article  Google Scholar 

  22. IBM Corporation (2004) An architectural blueprint for autonomic computing, 4th edn. Technical report

  23. Proctor M, Neale M, Lin P, Frandsen M (2008) Drools documentation. Technical report. JBoss.org

  24. Raspl S (2004) PMML version 3.0—overview and status. In: proceedings of the ACM workshop on data mining standards, services and platforms, Philadelphia, pp 18–22

  25. Haykin S (2008) Neural networks and learning machines. Prentice Hall, New York

    Google Scholar 

  26. Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  27. Compete III Vascular Tracker website. http://www.competestudy.com/

  28. Brause R, Langsdorf T, Hepp M (1999) Neural data mining for credit card fraud detection. In: ICTAI ’99: proceedings of the 11th IEEE international conference on tools with artificial intelligence, IEEE Computer Society, Washington, p 103

  29. Raffelt H, Margaria T, Steffen B, Merten M (2008) Hybrid test of web applications with webtest. In: Proceedings of the 2008 workshop on testing, analysis, and verification of web services and applications, ACM, New York, pp 1–7

Download references

Author information

Authors and Affiliations

  1. Department of Computing and Software, McMaster University, Hamilton, ON, Canada

    Mehran Najafi

  2. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Oshawa, ON, Canada

    Kamran Sartipi

Authors
  1. Mehran Najafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamran Sartipi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamran Sartipi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Najafi, M., Sartipi, K. Modeling service representatives in enterprise systems using generic agents. SOCA 5, 245–264 (2011). https://doi.org/10.1007/s11761-011-0088-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11761-011-0088-5

Keywords

Search

Navigation