Graph-Based Representations in Pattern Recognition and Computational Intelligence
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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The proliferation, diversity and complexity of genome databases pose a significant challenge to the multidatabase research community. We propose a meta-data framework based on a graph modeling technique and show how this framework can be applied to making two genome databases interoperable. Our technique maps individual database schemas expressed in heterogeneous data models into a common graph representation. This graph-based framework is designed to express both regular data and meta-data. Modeling both types of data is necessary for establishing database correspondences. We show how inter-database relationships can be expressed in terms of correspondences amongst basic graph units, including nodes, edges and the "value path". We apply these basic graph units to perform query interoperation between two databases, namely DB/12 (Database of Human Chromosome 12) and GDB (Genome Database).