% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Betancourt:893263,
      author       = {Betancourt, Clara and Hagemeier, Björn and Schröder,
                      Sabine and Schultz, Martin G.},
      title        = {{C}ontext aware benchmarking and tuning of a {TB}yte-scale
                      air quality database and web service},
      journal      = {Earth science informatics},
      volume       = {14},
      issn         = {1865-0473},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2021-02653},
      pages        = {1597-1607},
      year         = {2021},
      abstract     = {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 $32\%.$},
      cin          = {JSC / NIC},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406},
      pnm          = {511 - Enabling Computational- $\&$ Data-Intensive Science
                      and Engineering (POF4-511) / IntelliAQ - Artificial
                      Intelligence for Air Quality (787576) / Deep Learning for
                      Air Quality and Climate Forecasts $(deepacf_20191101)$ /
                      Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-511 / G:(EU-Grant)787576 /
                      $G:(DE-Juel1)deepacf_20191101$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34122663},
      UT           = {WOS:000658575200001},
      doi          = {10.1007/s12145-021-00631-4},
      url          = {https://juser.fz-juelich.de/record/893263},
}