Objects Description Exploiting User’s Sociality

Part of the Studies in Computational Intelligence book series (SCI, volume 374)


Declarative scene modelling is a very useful modelling technique which allows the user to create scenes by simply describing their wished properties and not the manner to construct them. In declarative modelling, solution filtering is a very important aspect due to the imprecise description of both the scene and the objects, as well as due to the subjective of humans regarding the content of a design is concerned. Currently, solution filtering is performed by the application of machine learning strategies or clustering methods in a collaborative or not framework. However, the main difficulty of these algorithms is that solution filtering is based on the usage of low-level attributes that describe either the scene or the object. This chapter addresses this difficulty by proposing a novel social oriented framework for solution reduction in a declarative modelling approach. In this case, we introduce semantic information in the organization of the users that participates in the filtering of the solutions. Algorithms derived from graph theory are presented with the aim to detect the most influent user with a social network (intra-social influence) or within different social groups (inter-social influence). Experimental results indicate the outperformance of the proposed social networking declarative modelling with respect to other methods.


Social networking declarative modelling computer graphics architect design 


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  1. 1.
    Dragonas, J., Makris, D., Lazaridis, A., Miaoulis, G., Plemenos, D.: Implementation of Collaborative Environment in MultiCAD Declarative Modelling System. In: International Conference on Computer Graphics and Artificial Intelligence (3IA 2005), Limoges, France, May 11-12 (2005)Google Scholar
  2. 2.
    Dragonas, J., Doulamis, N.: Web-based Collaborative System for Scene Modelling. In: Intelligent Scene Modelling Information Systems, pp. 121–151. Springer, Berlin (2009)CrossRefGoogle Scholar
  3. 3.
    Rodriguez, K., Al-Ashaab, A.: A Review of Internet based Collaborative Product Development Systems. In: Proceedings of the International Conference on Concurrent Engineering: Research and Applications, Cranfield, UK (2002)Google Scholar
  4. 4.
    Shen, W.: Web-based Infrastructure for Collaborative Product Design: An Overview. In: 6th International Conference on Computer Supported Cooperative Work in Design, Hong Kong, pp. 239–244 (2000)Google Scholar
  5. 5.
    Kvan, T.: Collaborative design: What is it? Automation in Construction 9(4), 409–415 (2000)CrossRefGoogle Scholar
  6. 6.
    Breslin, J., Decker, S.: The Future of Social Networks on the Internet- the need for semantics. IEEE Internet Computing, 86–90 (November-December 2007)Google Scholar
  7. 7.
    Ko, M.N., Cheek, G.P., Shehab, M., Sandhu, R.: Social Networks Connect Services. IEEE Computer Magazine, 37–43 (August 2010)Google Scholar
  8. 8.
    Douglis, F.: It’s All About the (Social) Network. IEEE Internet Computing, 4–6 (January-February 2010)Google Scholar
  9. 9.
    Plemenos, D., Miaoulis, G., Vassilas, N.: Machine Learning for a General Purpose Declarative Scene Modeller. In: International Conference on Computer Graphics and Vision (GraphiCon), Nizhny Novgorod (Russia), September 15-21 (2002)Google Scholar
  10. 10.
    Doulamis, A.: Dynamic Tracking Re-Adjustment: A Method for Automatic Tracking Recovery in Complex Visual Environments. Multimedia Tools and Applications 50(1), 49–73 (2010)CrossRefGoogle Scholar
  11. 11.
    Doulamis, A.: Adaptable Neural Networks for Objects’ Tracking Re-initialization. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 715–724. Springer, Heidelberg (2009), doi:10.1007/978-3-642-04277-5_72CrossRefGoogle Scholar
  12. 12.
    Bardis, G., Doulamis, N., Dragonas, J., Miaoulis, G., Plemenos, D.: A Parametric Mechanism for Preference Consensus in a Collaborative Declarative Design Environment. In: International Conference on Computer Graphics and Artificial Intelligent, Athens, Greece (2007)Google Scholar
  13. 13.
    Doulamis, N., Dragonas, J., Doulamis, A., Miaoulis, G., Plemenos, D.: Machine learning and pattern analysis methods for profiling in a declarative collaorative framework. In: Plemenos, D., Miaoulis, G. (eds.) Intelligent Computer Graphics 2009. Studies in Computational Intelligence, vol. 240, pp. 189–206. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Ng, Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Neural Information Processing Systems 14 (2002)Google Scholar
  15. 15.
    Delias, P., Doulamis, A., Matsatsinis, N.: A Joint Optimization Algorithm For Dispatching Tasks In Agent-Based Workflow Management Systems. In: International Conference on Enterprise Information Systems (ICEIS), Barcelona, Spain (June 2008)Google Scholar
  16. 16.
    Rui, Y., Huang, T.S.: Optimizing Learning in Image Retrieval. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (June 2000)Google Scholar
  17. 17.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall Press, Englewood Cliffs (1998)Google Scholar
  18. 18.
    Brandes, U.: Social network analysis and visualization. IEEE Signal Processing Magazine 25(6), 147–151 (2008)CrossRefGoogle Scholar
  19. 19.
    Dragonas, J., Doulamis, N.: Web-based collaborative system for scene modelling. In: Miaoulis, G., Plemenos, D. (eds.) Intel. Scene. Mod. Information Systems. SCI, vol. 181, pp. 121–151. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Doulamis, N., Bardis, G., Dragonas, J., Miaoulis, G.: Optimal recursive designers’ profile estimation in collaborative declarative environment. In: International Conference on Tools with Artificial Intelligence, ICTAI 2, art. no. 4410416, pp. 424–427 (2007)Google Scholar
  21. 21.
    Harold, A., Jay Sussman, G., Sussman, J.: Structure and Interpretation of Computer Programs, 2nd edn. MIT Press, Cambridge (1996)zbMATHGoogle Scholar
  22. 22.
    Kilker, J.: Conflict on collaborative design teams: understanding the role of social identities. IEEE Technology and Society Magazine 18(3), 12–21 (1999)CrossRefGoogle Scholar
  23. 23.
    Gummadi, G.P., Dunn, R.J., Saroiu, S., Gribble, S.D., Levy, H.M., Zahorjan, J.: Measurement, modeling and analysis of a peer-to-peer file-sharing workload. In: Proc. 19th ACM Symp. Operating Systems Principles (SOSP-19), pp. 314–329 (October 2003)Google Scholar
  24. 24.
    Liang, J., Kumar, R., Xi, Y., Ross, K.W.: Pollution in P2P file sharing systems. IEEE InfoCom 2, 1174–1185 (2005)Google Scholar
  25. 25.
    Vicky Zhao, H., Sabrina Lin, W., Ray Liu, K.J.: Behavior Modeling and Forensics for Multimedia Social Networks. IEEE Signal Processing Magazine, 118–139 (January 2009)Google Scholar
  26. 26.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  27. 27.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Barnes, J.A.: Class and committees in a Norwegian Island parish. Human Relations 7(1), 39–58 (1954)CrossRefGoogle Scholar
  29. 29.
    Freeman, L.C. (ed.): Social Network Analysis I–IV. Sage, Newbury Park (2008)Google Scholar
  30. 30.
    Pentland, A.S.: Social Signal Processing, pp. 108–111 (July 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.National Technical University of AthensAthensGreece
  2. 2.Department of Mathematics and Computer SciencesXLIM – UMR 6172 – CNRSLimoges CedexFrance
  3. 3.Department of InformaticsTechnological Education Institute of AthensEgaleoGreece
  4. 4.Department of Business AdministrationTechnological Education Institute of AthensEgaleoGreece

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