Integrating and analyzing medical and environmental data using ETL and Business Intelligence tools.

Abstract

Processing data that originates from different sources (such as environmental and medical data) can prove to be a difficult task, due to the heterogeneity of variables, storage systems, and file formats that can be used. Moreover, once the amount of data reaches a certain threshold, conventional mining methods (based on spreadsheets or statistical software) become cumbersome or even impossible to apply. Data Extract, Transform, and Load (ETL) solutions provide a framework to normalize and integrate heterogeneous data into a local data store. Additionally, the application of Online Analytical Processing (OLAP), a set of Business Intelligence (BI) methodologies and practices for multidimensional data analysis, can be an invaluable tool for its examination and mining. In this article, we describe a solution based on an ETL + OLAP tandem used for the on-the-fly analysis of tens of millions of individual medical, meteorological, and air quality observations from 16 provinces in Spain provided by 20 different national and regional entities in a diverse array for file types and formats, with the intention of evaluating the effect of several environmental variables on human health in future studies. Our work shows how a sizable amount of data, spread across a wide range of file formats and structures, and originating from a number of different sources belonging to various business domains, can be integrated in a single system that researchers can use for global data analysis and mining.