Federated database system for scientific data
Sang‐Chul Kim, Bongki Moon · 2018
Much like traditional databases, scientific data are managed in multiple separate databases by different sources and organizations. When such distributed data are analyzed together for more comprehensive understanding and prediction, it is necessary to access data via multiple simultaneous connections or collected in a single location. The inevitable consequence is, however, that a significant overhead is incurred due to differences in schemas, data transformation, and extraneous cost for storing intermediate data. This demo presents SDF, Scientific Database in Federation, which facilitates data sharing and exchange in order to support complex analytics with minimal integration overhead. SDF is currently implemented in SciDB using user-defined operators, providing two connection models, master-to-master and cluster-to-master, for a shared-nothing architecture.