Description: Context aware benchmarking and tuning of a TByte-scale air quality database and web service

This title appears in the Scientific Report : 2021

Context aware benchmarking and tuning of a TByte-scale air quality database and web service

We present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature o...

Personal Name(s):	Betancourt, Clara (Corresponding author)
	Hagemeier, Björn / Schröder, Sabine / Schultz, Martin G.
Contributing Institute:	Jülich Supercomputing Center; JSC John von Neumann - Institut für Computing; NIC
Published in:	Earth science informatics, 14 (2021) S. 1597-1607
Imprint:	Heidelberg Springer 2021
DOI:	10.1007/s12145-021-00631-4
Document Type:	Journal Article
Research Program:	Earth System Data Exploration Deep Learning for Air Quality and Climate Forecasts Artificial Intelligence for Air Quality Enabling Computational- & Data-Intensive Science and Engineering
Link:	OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/28504 in citations.
Please use the identifier: http://dx.doi.org/10.1007/s12145-021-00631-4 in citations.

We present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.