  Many fields of science rely on relational database management systems to
analyze, publish and share data. Since RDBMS are originally designed for, and
their development directions are primarily driven by, business use cases they
often lack features very important for scientific applications. Horizontal
scalability is probably the most important missing feature which makes it
challenging to adapt traditional relational database systems to the ever
growing data sizes. Due to the limited support of array data types and metadata
management, successful application of RDBMS in science usually requires the
development of custom extensions. While some of these extensions are specific
to the field of science, the majority of them could easily be generalized and
reused in other disciplines. With the Graywulf project we intend to target
several goals. We are building a generic platform that offers reusable
components for efficient storage, transformation, statistical analysis and
presentation of scientific data stored in Microsoft SQL Server. Graywulf also
addresses the distributed computational issues arising from current RDBMS
technologies. The current version supports load balancing of simple queries and
parallel execution of partitioned queries over a set of mirrored databases.
Uniform user access to the data is provided through a web based query interface
and a data surface for software clients. Queries are formulated in a slightly
modified syntax of SQL that offers a transparent view of the distributed data.
The software library consists of several components that can be reused to
develop complex scientific data warehouses: a system registry, administration
tools to manage entire database server clusters, a sophisticated workflow
execution framework, and a SQL parser library.

Databases