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. 2003:2003:514-8.

Contextualizing heterogeneous data for integration and inference

Zachary Pincus  1 , Mark A Musen
Affiliations

Contextualizing heterogeneous data for integration and inference

Zachary Pincus et al. AMIA Annu Symp Proc. 2003.

Abstract

Systems that attempt to integrate and analyze data from multiple data sources are greatly aided by the addition of specific semantic and metadata "context" that explicitly describes what a data value means. In this paper, we describe a systematic approach to constructing models of data and their context. Our approach provides a generic "template" for constructing such models. For each data source, a developer creates a customized model by filling in the tem-plate with predefined attributes and value. This approach facilitates model construction and provides consistent syntax and semantics among models created with the template. Systems that can process the template structure and attribute values can reason about any model so described. We used the template to create a detailed knowledge base for syndromic surveillance data integration and analysis. The knowledge base provided support for data integration, translation, and analysis methods.

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Figures

Figure 1
Figure 1. A Generic Structure for Data and their Metadata Context.
In our template ontology, data values are associated with metadata describing the semantic meaning of the data and absenteeism other relevant context. Arrows indicate one-to-one and one-to-many relationships between concepts.
Figure 2
Figure 2. The Template Ontology Customized for Syndromic Surveillance
Additions to the template ontology specific to syndromic surveillance are hilighted. At left is the structure of the template with our added context classes. At right are the top levels of the taxonomy of metadata attributes used to build LOINCContext objects. The vocabulary of “Measurable Properties” (shown partially expanded) was our primary addition.
Figure 3
Figure 3. Providing Context for Syndromic Surveillance.
Our template ontology describes the context of heterogeneous data and data sources. This context supports retrieving data and placing them in a consistent format (Data Broker) and transforming those data (Data Mapper) into new formats suitable for generic analytic methods.
See this image and copyright information in PMC

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