Incremental Representation and Management of Recursive Types in Graph-Based Data Model for Content Representation of Multimedia Data

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)


A data model incorporating the concepts of recursive graphs has been proposed for representing the contents of multimedia data. A shape graph, which represents the structure of a set of instances, has to catch their incremental updates. It is difficult to manage instances when they have recursive structure. This paper proposes a method of managing the recursive structure of instances. The procedure incrementally revising the information of the structure of shape graphs is presented. Owing to this procedure, the recursive structure could incrementally and properly be managed and represented in the shape graph.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Petrakis, E.G.M., Faloutsos, C.: Similarity Searching in Medical Image Databases. IEEE Trans. on Know. and Data Eng. 9, 435–447 (1997)CrossRefGoogle Scholar
  2. 2.
    Uehara, K., Oe, M., Maehara, K.: Knowledge Representation, Concept Acquisition and Retrieval of Video Data. In: Proc. of Int’l Symposium on Cooperative Database Systems for Advanced Applications, pp. 218–225 (1996)Google Scholar
  3. 3.
    Jaimes, A.: A Component-Based Multimedia A Data Model. In: Proc. of ACM Workshop on Multimedia for Human Communication: from Capture to Convey (MHC 2005), pp. 7–10 (2005)Google Scholar
  4. 4.
    Manjunath, B.S., Salembier, P., Sikora, T. (eds.): Introduction to MPEG-7. John Wiley & Sons, Ltd (2002)Google Scholar
  5. 5.
    Hochin, T.: Graph-Based Data Model for the Content Representation of Multimedia Data. In: Proc. of 10th Int’l Conf. on Knowledge-Based Intelligent Information and Eng. Systems (KES 2006), pp. 1182–1190 (2006)Google Scholar
  6. 6.
    Hochin, T., Nomiya, H.: A Logical and Graphical Operation of a Graph-based Data Model. In: Proc. of 8th IEEE/ACIS Int’l Conference on Computer and Information Science (ICIS 2009), pp. 1079–1084 (2009)Google Scholar
  7. 7.
    Hochin, T.: Decomposition of Graphs Representing the Contents of Multimedia Data. Journal of Communication and Computer 7(4), 43–49 (2010)Google Scholar
  8. 8.
    Ohira, Y., Hochin, T., Nomiya, H.: Introducing Specialization and Generalization to a Graph-Based Data Model. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS, vol. 6884, pp. 1–13. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Silberschatz, A., Korth, H., Sudarshan, S.: Database System Concepts, 4th edn. McGraw-Hill (2002)Google Scholar
  10. 10.
    Tanaka, K., Nishio, S., Yoshikawa, M., Shimojo, S., Morishita, J., Jozen, T.: Obase Object Database Model: Towards a More Flexible Object-Oriented Database System. In: Proc. of Int’l. Symp. on Next Generation Database Systems and Their Applications (NDA 1993), pp. 159–166 (1993)Google Scholar
  11. 11.
    Goldman, R., Widom, J.: DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases. In: Proc. of 23rd Int’l Conf. on Very Large Databases, pp. 436–445 (1997)Google Scholar
  12. 12.
    Nestorov, S., Ullman, J., Wiener, J., Chawathe, S.: Representative Objects: Concise Representations of Semistructured, Hierarchical Data. In: Proc. of 13th Int’l Conf. on Data Engineering (ICDE 1997), pp. 79–90 (1997)Google Scholar
  13. 13.
    Soe, D.-Y., Lee, D.-H., Moon, K.-S., Chang, J., Lee, J.-Y., Han, C.-Y.: Schemaless Representation of Semistructured Data and Schema Construction. In: Tjoa, A.M. (ed.) DEXA 1997. LNCS, vol. 1308, pp. 387–396. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  14. 14.
    Wang, Q.Y., Yu, J.X., Wong, K.-F.: Approximate Graph Schema Extraction for Semi-structured Data. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 302–316. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  15. 15.
    Chidlovskii, B.: Schema Extraction from XML Data: a Grammatical Inference Approach. In: Proc. of 8th Int’l Workshop on Knowledge Representation Meets Databases, KRDB 2001 (2001)Google Scholar
  16. 16.
    Garofalakis, M., Gionis, A., Rastogi, R., Seshadri, S., Shim, K.: XTRACT: Learning Document Type Descriptors from XML Document Collections. In: Data Mining and Knowledge Discovery, vol. 7, pp. 23–56. Kluwer Academic Publishers (2003)Google Scholar
  17. 17.
    Bex, G.J., Neven, F., Schwentick, T., Vansummere, S.: Inference of Concise Regular Expressions and DTDs. ACM Trans. on Database Systems 35(2) (2010)Google Scholar
  18. 18.
    Bex, G.J., Gelade, W., Neven, F., Vansummere, S.: Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data. ACM Trans. on the Web 4(4) (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Kyoto Institute of TechnologySakyo-kuJapan

Personalised recommendations