Statistical and Scientific Database Issues
IEEE Transactions on Software Engineering
Summary of the final report of the NSF workshop on scientific database management
ACM SIGMOD Record - Directions for future database research & development
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Data warehousing in an integrated health system: building the business case
Proceedings of the 1st ACM international workshop on Data warehousing and OLAP
Design and management of data warehouses report on the DMDW'99 workshop
ACM SIGMOD Record
A survey of logical models for OLAP databases
ACM SIGMOD Record
Managing Derived Data in the Gaea Scientific DBMS
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Scientific Databases - State of the Art and Future Directions
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Research Issues in Clinical Data Warehousing
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Multidimensional Data Modeling for Complex Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Data warehousing imposes itself as an attractive solution for centralizing and analyzing high quality data. In the medical research field, this technology can be used to validate assumptions and to discover trends on large amount of patient data. However, like other scientific complex data, medical data and especially raw sensor data need to be processed before becoming interpretable. The selection of the process mode is a key issue in the physician's reasoning. In our study, we propose a solution to gather data and processes into a single information warehouse. Our solution provides features for loading, modeling and querying the information warehouse. Stored data are multidimensional data (patient identity, therapeutic data...), raw sensor data (electrocardiogram, X-ray...) and processes. A prototype has been implemented and is illustrated in the cardiology domain to finalise processes in order to detect heart arrhythmia and acute myocardial ischemia that may lead to sudden cardiac death.