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000893263 1001_ $$0P:(DE-Juel1)171435$$aBetancourt, Clara$$b0$$eCorresponding author
000893263 245__ $$aContext aware benchmarking and tuning of a TByte-scale air quality database and web service
000893263 260__ $$aHeidelberg$$bSpringer$$c2021
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000893263 520__ $$aWe present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.
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000893263 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x1
000893263 536__ $$0G:(DE-Juel1)deepacf_20191101$$aDeep Learning for Air Quality and Climate Forecasts (deepacf_20191101)$$cdeepacf_20191101$$fDeep Learning for Air Quality and Climate Forecasts$$x2
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000893263 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, Björn$$b1
000893263 7001_ $$0P:(DE-Juel1)16212$$aSchröder, Sabine$$b2
000893263 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin G.$$b3
000893263 773__ $$0PERI:(DE-600)2423990-2$$a10.1007/s12145-021-00631-4$$p1597-1607$$tEarth science informatics$$v14$$x1865-0473$$y2021
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