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Scientific and Statistical Database Management, International Conference on (1996)
Stockholm, SWEDEN
June 18, 1996 to June 20, 1996
ISBN: 0-8186-7264-1
pp: 220
Rob Sargent , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Dave Fuhrman , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Terence Critchlow , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Tony Di Sera , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Robert Mecklenburg , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Gary Lindstrom , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
Peter Cartwright , Utah Center For Human Genome Research, Department of Computer Science, University of Utah
ABSTRACT
The Human Genome Project poses severe challenges in database design and implementation. These include comprehensive coverage of diverse data domains and user constituencies; robustness in the presence of incomplete, inconsistent and multi-version data; accessibility through many levels of abstraction, and scalability in content and organizational complexity. This paper presents a new data model developed to meet these challenges by the Utah Center for Human Genome Research. The central characteristics of this data model are (i) a high level data model comprising five broadly applicable workflow notions; (ii) representation of those notions as objects in an extended relational model; (iii) expression of working database schemas as meta data in administration tables; (iv) population of the database through tables dependent on the meta data tables, and (v) implementation via a conventional relational database management system. We explore two advantages of this approach: the resulting representational flexibility, and the reflective use of meta data to accomplish schema evolution by ordinary updates. Implementation and performance pragmatics of this work are sketched, as well as implications for future database development.
INDEX TERMS
extended relational data model, schema evolution, metadata, reflection, genome informatics.
CITATION

D. Fuhrman et al., "The Design and Implementation of a Database For Human Genome Research," Scientific and Statistical Database Management, International Conference on(SSDBM), Stockholm, SWEDEN, 1996, pp. 220.
doi:10.1109/SSDM.1996.506064
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