Data Warehousing for Scientific Behavioral Data

Building a data management model for scientific applications poses challenges not normally encountered in commercial database development. Complex relationships and data types, evolving schema, and large data volumes (in terabyte) are some commonly cited challenges with scientific data. In this thesis, we propose a data warehouse model to manage and analyze scientific behavioral data. Data warehousing is popular in customer-centered environments and encapsulates the process of transforming and aggregating operational data and bringing it to a platform optimized for Online Analytical Processing (OLAP). A database schema ubiquitous with data warehousing is the dimensional or the star schema. In this paper, we develop a proof-of-concept data warehouse system for a scientific laboratory at Queen’s University that is conducting behavioral studies in the area of limb kinematics. The system is based on three primary technologies: a Perl based parsing grammar for transforming and cleaning source data, an object-relational data management system based on IBM’s Universal DB2 system, and a Java-based front-end interface that is accessible through MathWork Inc’s Matlab system.