Considerable work has been taken by researchers to address the need for temporal data deduction in biomedical applications, but relatively little research has examined how to create robust, efficient approaches for such methods using large databases. We present the design and evaluation of a distributed architecture that can be dynamically optimized to perform large-scale abstraction of temporal data.
