  Organizations are often faced with the challenge of providing data management
solutions for large, heterogenous datasets that may have different underlying
data and programming models. For example, a medical dataset may have
unstructured text, relational data, time series waveforms and imagery. Trying
to fit such datasets in a single data management system can have adverse
performance and efficiency effects. As a part of the Intel Science and
Technology Center on Big Data, we are developing a polystore system designed
for such problems. BigDAWG (short for the Big Data Analytics Working Group) is
a polystore system designed to work on complex problems that naturally span
across different processing or storage engines. BigDAWG provides an
architecture that supports diverse database systems working with different data
models, support for the competing notions of location transparency and semantic
completeness via islands and a middleware that provides a uniform multi--island
interface. Initial results from a prototype of the BigDAWG system applied to a
medical dataset validate polystore concepts. In this article, we will describe
polystore databases, the current BigDAWG architecture and its application on
the MIMIC II medical dataset, initial performance results and our future
development plans.
