skip to main content
article

Database support for unstructured meshes

Published:01 August 2013Publication History
Skip Abstract Section

Abstract

Despite ubiquitous usage of unstructured mesh in many application domains (e.g., computer aided design, scientific simulation, climate modeling, etc.), there is no specialized mesh database which supports storing and querying such data structures. Existing mesh libraries use file-based APIs which do not support declarative querying and are difficult to maintain. A mesh database can benefit these domains in several ways such as: declarative query language, ease of maintenance, query optimization, etc. In this thesis work, the core idea is to have a very general model which can represent objects from different domains and specialize it to smaller object classes using combinatorial constraints. We propose the Incidence multi-Graph Complex (ImG-Complex) data model for storing combinatorial aspect of meshes in a database. We extend incidence graph (IG) representation with multi-incidence information (ImG) to represent a class of objects which we call ImG-Complexes. ImG-Complex can support a wide range of application domains. We introduce optional and application-specific constraints to restrain the general ImG model to specific object classes or specific geometric representations. The constraints check validity of meshes based on the properties of the modeled object class. Finally, we show how graph databases can be utilized and reused to query some combinatorial mesh queries based on the (possibly constrained) ImG model. In particular, we show the strengths and limitations of a graph-only query language in expressing combinatorial mesh queries.

References

  1. http://neo4j.org.Google ScholarGoogle Scholar
  2. R. Angles and C. Gutierrez. Survey of graph database models. ACM Comput. Surv., 40(1):1:1-1:39, Feb. 2008. Google ScholarGoogle Scholar
  3. P. Baumann. A formal specification of a boundary representation. In Eurographics, volume 88, pages 141-154, 1988.Google ScholarGoogle Scholar
  4. B. G. Baumgart. A polyhedron representation for computer vision. In Proc. National computer conference and exposition, AFIPS'75, pages 589-596, New York, NY, USA, 1975. ACM. Google ScholarGoogle Scholar
  5. G. Berti. Generic software components for Scientific Computing. PhD thesis, BTU Cottbus, 2000.Google ScholarGoogle Scholar
  6. G. Berti. Gral - the grid algorithms library. Future Generation Computer Systems, 22, 2006. Google ScholarGoogle Scholar
  7. E. Brisson. Representing geometric structures in d dimensions: topology and order. In Proc. 5th ann. symp. on Computational geometry, SCG'89, pages 218-227, New York, NY, USA, 1989. ACM. Google ScholarGoogle Scholar
  8. V. A. et al. High resolution forward and inverse earthquake modeling on terascale computers. In SC, page 52, 2003. Google ScholarGoogle Scholar
  9. B. Howe. Gridfields: model-driven data transformation in the physical sciences. PhD thesis, Portland, OR, USA, 2007. AAI3255425. Google ScholarGoogle Scholar
  10. B. Howe and D. Maier. Algebraic manipulation of scientific datasets. In Proc. 30th Int'l Conf. on Very large data bases, VLDB'04, pages 924-935, 2004. Google ScholarGoogle Scholar
  11. B. Levy and J.-L. Mallet. Cellular modelling in arbitrary dimension using generalized maps. Technical report, ISA-GOCAD (Inria-Lorraine/CNRS), 1999.Google ScholarGoogle Scholar
  12. P. Lienhardt. Topological models for boundary representation: a comparison with n-dimensional generalized maps. Comput. Aided Des., 23(1):59-82, Feb. 1991. Google ScholarGoogle Scholar
  13. P. Lienhardt, L. Fuchs, Y. Bertrand, et al. Combinatorial models for topology-based geometric modeling. Theory and applications of proximity, nearness and uniformity, pages 151-198, 2009.Google ScholarGoogle Scholar
  14. D. W. Moore. Simplicial mesh generation with applications. PhD thesis, Ithaca, NY, USA, 1992. UMI Order No. GAX93-00795. Google ScholarGoogle Scholar
  15. OGC. Opengis simple features specification for sql. Technical report, Revision 1.0, 1998.Google ScholarGoogle Scholar
  16. C. Pain, M. Piggott, A. Goddard, F. Fang, G. Gorman, D. Marshall, M. Eaton, P. Power, and C. De Oliveira. Three-dimensional unstructured mesh ocean modelling. Ocean Modelling, 10(1):5-33, 2005.Google ScholarGoogle Scholar
  17. C. Silva, Y. jen Chiang, W. Corrêa, J. El-sana, and P. Lindstrom. Out-of-core algorithms for scientific visualization and computer graphics. In Visualization 2002 Course Notes, 2002.Google ScholarGoogle Scholar
  18. G. M. Ziegler. Lectures on polytopes. Springer-Verlag, New York, 1995.Google ScholarGoogle Scholar

Index Terms

  1. Database support for unstructured meshes
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image Proceedings of the VLDB Endowment
      Proceedings of the VLDB Endowment  Volume 6, Issue 12
      August 2013
      264 pages

      Publisher

      VLDB Endowment

      Publication History

      • Published: 1 August 2013
      Published in pvldb Volume 6, Issue 12

      Qualifiers

      • article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader