Advertisement

Spatio-temporal and Multi-representation Modeling: A Contribution to Active Conceptual Modeling

  • Stefano Spaccapietra
  • Christine Parent
  • Esteban Zimányi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4512)

Abstract

Worldwide globalization increases the complexity of problem solving and decision-making, whatever the endeavor is. This calls for a more accurate and complete understanding of underlying data, processes and events. Data representations have to be as accurate as possible, spanning from the current status of affairs to its past and future statuses, so that it becomes feasible, in particular, to elaborate strategies for the future based on an analysis of past events. Active conceptual modeling is a new framework intended to describe all aspects of a domain. It expands the traditional modeling scope to include, among others, the ability to memorize and use knowledge about the spatial and temporal context of the phenomena of interest, as well as the ability to analyze the same elements under different perspectives. In this paper we show how these advanced modeling features are provided by the MADS conceptual model.

Keywords

Active conceptual models spatio-temporal information multiple representations multiple perspectives MADS model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, P.P., Thalheim, B., Wong, L.Y.: Future Directions of Conceptual Modeling. In: Chen, P.P., Akoka, J., Kangassalu, H., Thalheim, B. (eds.) Conceptual Modeling. LNCS, vol. 1565, pp. 287–301. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Parent, C., Spaccapietra, S., Zimányi, E.: Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  3. 3.
    Parent, C., Spaccapietra, S., Zimányi, E.: The MurMur Project: Modeling and Querying Multi-Represented Spatio-Temporal Databases. Information Systems 31(8), 733–769 (2006)CrossRefGoogle Scholar
  4. 4.
    Chen, P.P., Wong, L.Y.: A Proposed Preliminary Framework for Conceptual Modeling of Learning from Surprises. In: ICAI 2005. Proceedings of the International Conference on Artificial Intelligence, pp. 905–910. CSREA Press (2005)Google Scholar
  5. 5.
    Khatri, V., Ram, S., Snodgrass, R.: Augmenting a Conceptual Model with Geospatiotemporal Annotations. IEEE Transctions on Knowledge and Data Engineering 16, 1324–1338 (2004)CrossRefGoogle Scholar
  6. 6.
    Pelekis, N., Theodooulidis, B., Kopanakis, I., Theodoridis, Y.: Literature review of spatio-temporal database models. The Knowledge Engineering Review 19, 235–274 (2004)CrossRefGoogle Scholar
  7. 7.
    Bédard, Y., Bernier, E.: Supporting Multiple Representations with Spatial Databases Views Management and the Concept of VUEL. In: Proceedings of the Joint Workshop on Multi-Scale Representations of Spatial Data, ISPRS WG IV/3, ICA Comm. on Map Generalization (2002)Google Scholar
  8. 8.
    Malinowski, E., Zimányi, E.: Designing Conventional, Spatial, and Temporal Data Warehouses: Concepts and Methodological Framework. Springer, Heidelberg (to appear, 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Stefano Spaccapietra
    • 1
  • Christine Parent
    • 2
  • Esteban Zimányi
    • 3
  1. 1.Database Laboratory, Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.HEC ISIUniversity of LausanneLausanneSwitzerland
  3. 3.Department of Computer & Decision Engineering (CoDE)Université Libre de BruxellesBruxellesBelgium

Personalised recommendations