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

V. S. Subrahmanian

doi:10.1117/12.200784

31 January 1995 Hybrid knowledge bases for integrating symbolic, numeric, and image data

V. S. Subrahmanian

Proceedings Volume 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; (1995) https://doi.org/10.1117/12.200784
Event: 23 Annual AIPR Workshop: Image and Information Systems: Applications and Opportunities, 1994, Washington, DC, United States

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.

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V. S. Subrahmanian "Hybrid knowledge bases for integrating symbolic, numeric, and image data", Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); https://doi.org/10.1117/12.200784

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KEYWORDS

Databases

Chemical species

Image segmentation

Information fusion

Surveillance

Computer architecture

Computer programming

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