Hybrid knowledge bases for integrating symbolic, numeric, and image data

V. S. Subrahmanian*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A hybrid knowledge base (HKB), due to Nerode and Subrahmanian, is a formalism that provides a uniform theoretical framework within which heterogeneous data representation paradigms may be integrated. The HKB framework is broad enough to support the integration of a wide array of databases including, but not restricted to: relational data (with multiple schemas), spatial data structures (including different kinds of quadtrees), pictorial data (including GIF files), numeric data and computations (e.g., linear and integer programming), and terrain data. In this paper, we focus on how the HKB paradigm can be used as a unifying framework to reason about terrain data in the context of background data that may be contained in relational and spatial data structures. We show how the current implementation of the HKB compiler can support such an integration scheme.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsPeter J. Costianes
Pages76-87
Number of pages12
StatePublished - 1995
Externally publishedYes
Event23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities - Washington, DC, USA
Duration: Oct 12 1994Oct 14 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2368
ISSN (Print)0277-786X

Conference

Conference23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities
CityWashington, DC, USA
Period10/12/9410/14/94

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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