2013/08/06 by László Dobos, Dobos, László, Alexander S. Szalay +7
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Databases (cs.DB) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Scientific Computing and Data Management #cs.DB
paper · pdf · doi:10.48550/arxiv.1308.1440
SSDBM 2013 proceedings
arxiv created 2013/08/06 · openalex publication_date 2013/08/06 · arxiv updated 2013/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.