Advertisement

World Wide Web

, Volume 16, Issue 4, pp 379–398 | Cite as

Adaptive support framework for wisdom web of things

  • Yang GaoEmail author
  • Mufeng Lin
  • Ruili Wang
Article

Abstract

Wisdom Web of Things (W2T) is the next generation of networks, which provides ubiquitous wisdom services in a ubiquitous network in the hyper world. Adaptiveness is the key issue of realizing the harmonious unity of human-information-thing. This paper proposes a self-adaptive support framework for W2T, which has three important components: (i) An adaptive requirement description language, which is to describe the wisdom service models and self-adaptive wisdom service strategies. (ii) Forward reasoning and backward planning ability. We propose that forward reasoning can be implemented based on the Rete algorithm and backward planning can be implemented based on a Hierarchical Task Network (HTN), which enable W2T to achieve complex, rapid, and efficient reasoning and planning to provide active, transparent, safe, and reliable services. (iii) A knowledge base evolution mechanism based on a learning classifier system, which is to realize the evolution of the knowledge base, and to satisfy the dynamic requirements of wisdom services. We take a wisdom traffic system as an example to demonstrate the data conversion mechanism and the functions of the proposed self-adaptive support framework.

Keywords

wisdom web of things adaptive suppport framework adaptive requirement description language Rete hierarchical task network learning classifier system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bertino, E., Ferrari, E., Perego, A.: A general framework for web content filtering. World Wide Web 13(3), 215–249. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Bruno, E., Faessel, N., Glotin, H., Le Maitre, J., Scholl, M.: Indexing and querying segmented web pages: the BlockWeb Model. World Wide Web 14(5–6), 623–649. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Bull, L., Studley, M., Bagnall, A., Whittley: Learning classifier system ensembles with rule-sharing. IEEE Trans. Evol. Comput. 11(4), 496–502 (2007)CrossRefGoogle Scholar
  4. 4.
    Cheng, S.-W.: Rainbow: Cost-effective software architecture-based self-adaptation. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, May 2008. Technical Report CMU-ISR-08-113Google Scholar
  5. 5.
    Erol, K.: Hierarchical Task Network Planning: Formalization, Analysis, and Implementation. Ph.D. Thesis. University of Maryland, College Park (1995)Google Scholar
  6. 6.
    Forgy, C.: Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell. 19:17–37 (1982)Google Scholar
  7. 7.
    Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complex. 19, 1–8 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Huang, H., Liu, C., Zhou, X.: Approximating query answering on RDF databases. World Wide Web 15(1), 89–114. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Lekavý, M., Návrat, P.: Expressivity of STRIPS-Like and HTN-Like Planning. In Agent and Multi-Agent Systems: technologies and applications, Proc. of 1st KES Int. Symp. KES-AMSTA 2007, pp. 121–130 (2007)Google Scholar
  10. 10.
    Li, J.-Q., Zhao, Y., Garcia-Molina, H.: A path-based approach for web page retrieval. World Wide Web 15(3), 257–283. Springer, Heidelberg (2012)Google Scholar
  11. 11.
    Liu, J.: Web Intelligence (WI): What makes wisdom web?. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (Aca-pulco, Mexico, Aug. 9–15). Morgan Kaufmann, San Francisco, 1596–1601 (2003)Google Scholar
  12. 12.
    Ma, J.: Smart u-things-challenging real world complexity. IPSJ Symp. Series 2005(19), 146–150 (2005)Google Scholar
  13. 13.
    Ma, J.: Active smart u-things and cyber individuals. active media technology. Lect. Notes Comput. Sci. 6335, 5 (2005)CrossRefGoogle Scholar
  14. 14.
    Ma, J.: Smart u-things and ubiquitous intelligence. In: Proc the 2nd international conference on embedded software and systems (ICESS 2005), p. 776Google Scholar
  15. 15.
    Salehie, M., Tahvildari, L.: Self-adaptive software: landscape and research challenges. ACM Trans. Autonom. Adapt. Syst. 4(2), 1–42 (2009)CrossRefGoogle Scholar
  16. 16.
    Sensoy, M., Yolum, P.: Automating user reviews using ontologies: an agent-based approach. World Wide Web 15(3), 285–323. Springer, Heidelberg (2012)Google Scholar
  17. 17.
    Sottara, D., Mello, P., Proctor, M.: A configurable Rete-OO engine for reasoning with different types of imperfect information. IEEE Trans. Knowl. Data Eng. 22(11), 1535–1548 (2010)CrossRefGoogle Scholar
  18. 18.
    Squicciarini, A.C., Sundareswaran, S.: Web-Traveler policies for images on social networks. World Wide Web 12(4), 461–484. Springer, Heidelberg (2009)Google Scholar
  19. 19.
    Tao, X., Li, Y., Zhong, N.: A personalized ontology model for web information gathering. IEEE Trans. Knowl. Data Eng. 23(4), 496–511 (2011)CrossRefGoogle Scholar
  20. 20.
    WI-IAT 2011 Panel on Wisdom Web of Things (W2T): Fundamental issues, challenges and potential applications. Available at: http://wi-iat-2011.org/
  21. 21.
    Wu, X., Ngo, C.-W., Zhu, Y.-M., Peng, Q.: Boosting web video categorization with contextual information from social web. World Wide Web 15(2), 197–212. Springer, Heidelberg (2012)Google Scholar
  22. 22.
    Yang, Y., Huang, Z., Shen, H.T., Zhou, X.: Mining multi-tag association for image tagging. World Wide Web 14(2), 133–156. Springer, Heidelberg (2011)Google Scholar
  23. 23.
    Yang, J., Pui, G., Fung, C., Lu, W., Zhou, X., Chen, H., Du, X.: Finding superior skyline points for multidimensional recommendation applications. World Wide Web 15(1), 33–60. Springer, Heidelberg (2012)Google Scholar
  24. 24.
    Yao, Y.Y., Zhong, N., Liu, J., Ohsuga, S.: Web Intelligence (WI) research challenges and trends in the new information age. In: Zhong, N., Yao, Y.Y., Liu, J., Ohsuga, S. (eds.) Web intelligence: research and development. LNAI, 2198, pp. 1–17 Springer, Berlin (2001)Google Scholar
  25. 25.
    Zhong, N.: Impending Brain Informatics research from Web intelligence perspective. Int. J. Inf. Technol. Decis. Mak. 5(4), 713–727 (2006)CrossRefGoogle Scholar
  26. 26.
    Zhong, N.: Ways to develop human-level web intelligence: a brain informatics perspective. In: Franconi, E., Kifer, M., May, W. (eds.) The Semantic Web: Research and Applications. LNCS, 4519, pp. 27–36. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Qin, Y., Li, K. and Wah, B.: Web intelligence meets brain informatics. State-of-the-art-survey, LNCS 4845, pp. 223–243. Springer, Berlin (2007)Google Scholar
  28. 28.
    Zhong, N., Liu, J., Yao, Y.: In search of the wisdom web. IEEE Comput. 35(11), 27–31 (2002)CrossRefGoogle Scholar
  29. 29.
    Zhong, N., Liu, J., Yao, Y.Y.: Envisioning intelligent information technologies through the prism of web intelligence. Commun. ACM 50(3), 89–94 (2007)CrossRefGoogle Scholar
  30. 30.
    Zhong, N., Liu, J., Yao, Y.Y., Ohsuga, S.: Web Intelligence (WI). In: Proc the 24th IEEE computer society international computer software and applications conference (COMPSAC 2000), pp 469–470Google Scholar
  31. 31.
    Zhong, N., Ma, J.H., Huang, R.H., Liu, J.M., Yao, Y.Y., Zhang, Y.X. and Chen, J.H.: Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomput., 1–21 (2010)Google Scholar
  32. 32.
    Zhong, N.: Building a Brain-informatics portal on the Wisdom Web with a multi-layer grid: a new challenge for web intelligence research. In: Torra, V. et al. (eds.) Modeling decisions for artificial intelligence. LNAI 3558, pp. 24–35. Springer, Berlin (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

Personalised recommendations