Contextualizing heterogeneous data for integration and inference.

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.