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@ARTICLE{Leuridan:1052199,
      author       = {Leuridan, Mathilde and Hawkes, James and Smart, Simon and
                      Danovaro, Emanuele and Schultz, Martin and Quintino, Tiago},
      title        = {{P}olytope: an algorithm for efficient feature extraction
                      on hypercubes},
      journal      = {Journal of Big Data},
      volume       = {12},
      number       = {1},
      issn         = {2196-1115},
      address      = {Heidelberg [u.a.]},
      publisher    = {SpringerOpen},
      reportid     = {FZJ-2026-00834},
      pages        = {243},
      year         = {2025},
      abstract     = {Data extraction algorithms on data hypercubes, or
                      datacubes, are traditionally only capable of cutting boxes
                      of data along the datacube axes. For many use cases however,
                      this returns much more data than users actually need,
                      leading to an unnecessary consumption of I/O resources. In
                      this paper, we propose an alternative feature extraction
                      technique, which carefully computes the indices of data
                      points contained within user-requested shapes. This enables
                      data storage systems to only read and return bytes useful to
                      user applications from the datacube. Our main algorithm is
                      based on high-dimensional computational geometry concepts
                      and operates by successively reducing polytopes down to the
                      points contained within them. We analyse this algorithm in
                      detail before providing results about its performance and
                      scalability. In particular, we show it is possible to
                      achieve data reductions of up to $99\%$ using this algorithm
                      instead of current state of practice data extraction
                      methods, such as meteorological field extractions from
                      ECMWF’s FDB data store, where feature shapes are extracted
                      a posteriori as a post-processing step. As we discuss later
                      on, this novel extraction method will considerably help
                      scale access to large petabyte size data hypercubes in a
                      variety of scientific fields.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1186/s40537-025-01306-3},
      url          = {https://juser.fz-juelich.de/record/1052199},
}