In this context, the warehouse measures, though not necessarily numerical, remain the indicators for analysis, and analysis is still performed following different perspectives represented by dimensions. Large data volumes and their dating are other arguments in favor of this approach (Darmont et al., 2003). Data warehousing can also support various types of analysis, such as statistical reporting, on-line analysis (OLAP) and data mining. The aim of this article is to present an overview of the existing data warehouses for biomedical data and to discuss the issues and future trends in biomedical data warehousing. We illustrate this topic by presenting the design of an innovative, complex data warehouse for personal, anticipative medicine.
Key Terms in this Chapter
Information-Based Medicine: Utilizing information technology to achieve personalized health care (Saad, 2004).
OLAP (Online Analytical Processing): An approach for processing decision-support, analytical queries that are dimensional in nature.
ETL: Data warehousing process that includes extracting data from external sources, transforming them and finally loading them into the warehouse.
Bus Architecture: Set of conformed dimensions and standardized definitions of facts (Kimball & Ross, 2002). Datamarts “plug into” this bus to receive the dimensions and facts they need (Firestone, 2002).
Data Warehouse: Subject-oriented, integrated, time-variant and nonvolatile collection of data in support of management’s decision-making process (Inmon, 2002).
Complex Data: Data that are not numerical or symbolic (e.g., multimedia, heterogeneous data stored on multiple platforms).
Datamart: Logical and physical subset of the overall data warehouse, usually dedicated to a given activity.