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Abstract. Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

Abstract

Abstract. When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies, problems arise when combining knowledge across domains and the knowledge is simply merged. Also, knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. An algebra over ontologies has been proposed to support disciplined manipulation of domain knowledge resources. However, if one tries to interoperate directly with the knowledge bases, semantic problems arise due to heterogeneity of representations. This heterogeneity problem can be eliminated by using an intermediate model that controls the knowledge translation from a source knowledge base. The intermediate model we h a ve developed is based on the concept of abstract knowledge representation and has two components: a modeling behavior which separates the knowledge from its implementation, and a performative behavior which establishes context abstraction rules over the knowledge.

We present a radically different approach to the recovery of the three dimensional geometric and reflectance properties of a surface from image data. We pose the problem in a Bayesian framework, and proceed to infer the parameters of the model describing the surface. This allows great flexibility in the specification of the model, in terms of how both the geometrical properties and surface reflectance are specified. In the usual manner for Bayesian approaches it requires that we can simulate the data that would have been recorded for any state of the model in order to infer the model. The theoretical aspects are thus very general. We present results for one type of surface geometry (the triangular mesh) and for the Lambertian model of light scattering. Our framework also allows the easy incorporation of data from multiple sensing modalities.