The human genome project and informatics
Communications of the ACM
An overview of the object protocol model (OPM) and the OPM data management tools
Information Systems - Special issue: databases: their creation, management and utilization
Building a Laboratory Information System Around a C++-Based Object-Oriented DBMS
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Computational Proxies: Modeling Scientific Applications in Object Databases
Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management
Constructing a Domain-Specific DBMS using a Persistent Object System
Proceedings of the Sixth International Workshop on Persistent Object Systems
Migrating relational data to an ODBMS: strategics and lessons from a molecular biology experience
Proceedings of the 12th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A framework for user driven data management
Information Systems
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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.