In this example, the GE Healthcare Informatics wrappers effectively act as a collection level to capture, pre-process and propagate the data into the larger data universe. This collection level also readily supports the conversion of non-standard data when required to allow accessibility to other systems.
Simplifying Data Complexity
The data complexity within this space exists at many different levels, with diversity both geographically and structurally, without consistency in naming, formats or access methods. The discoveryHub technology provides the means to manage all of these issues.
The GE Healthcare Informatics system is agnostic to geographical and structural diversity of data sources. The wrapper technology addresses the geographically dispersed nature of data by presenting each source as if it were local. The query engine allows each data source to be transposed into any model, thus solving the issue of structural inconsistency. Finally the ontology level allows the creation of a consistent data model that maps all physical data into a definable ontological data model. The ontology enables us to understand the relationships between data sources and must eventually become all encompassing in this field. The transformation level enables us to take the related data sets and provide a layer to enable the physical conversion between potentially many data sources.
Ultimately, the discoveryHub approach allows the complex universe of data to be transformed into a targeted, focused view of the relevant pieces of information from an unlimited set of data sources. In essence, this allows the researcher to extract the particular “trees” of inte