Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we propose SciQL (pronounced as ‘cycle’), the first SQL-based query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence- interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., fixed-sized and unbounded groups based on explicit relationships between elements positions. This leads to a generalisation of window-based query processing with wide applicability in science domains. This paper describes the main language features of SciQL and illustrates it using time-series concepts.

Additional Metadata
Keywords sciql, array query language, scientific data, time series data processing
THEME Information (theme 2)
Publisher ACM New York, NY, USA
ISBN 978-1-4503-0627-0
Conference International Database Engineering and Applications Symposium
Zhang, Y, Kersten, M.L, Ivanova, M.G, & Nes, N.J. (2011). SciQL, Bridging the Gap between Science and Relational DBMS. ACM New York, NY, USA.