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@INPROCEEDINGS{Barakat:902553,
      author       = {Barakat, Chadi and Riedel, Morris and Brynjolfsson, S. and
                      Cavallaro, Gabriele and Busch, Josefine and Sedona, Rocco},
      title        = {{D}esign and {E}valuation of an {HPC}-based {E}xpert
                      {S}ystem to speed-up {R}etail {D}ata {A}nalysis using
                      {R}esidual {N}etworks {C}ombined with {P}arallel
                      {A}ssociation {R}ule {M}ining and {S}calable {R}ecommenders},
      reportid     = {FZJ-2021-04354},
      pages        = {274 - 279},
      year         = {2021},
      abstract     = {Given the Covid-19 pandemic, the retail industry shifts
                      many business models to enable more online purchases that
                      produce large transaction data quantities (i.e., big data).
                      Data science methods infer seasonal trends about products
                      from this data and spikes in purchases, the effectiveness of
                      advertising campaigns, or brand loyalty but require
                      extensive processing power leveraging High-Performance
                      Computing to deal with large transaction datasets. This
                      paper proposes an High-Performance Computing-based expert
                      system architectural design tailored for ‘big data
                      analysis’ in the retail industry, providing data science
                      methods and tools to speed up the data analysis with
                      conceptual interoperability to commercial cloud-based
                      services. Our expert system leverages an innovative Modular
                      Supercomputer Architecture to enable the fast analysis by
                      using parallel and distributed algorithms such as
                      association rule mining (i.e., FP-Growth) and recommender
                      methods (i.e., collaborative filtering). It enables the
                      seamless use of accelerators of supercomputers or
                      cloud-based systems to perform automated product tagging
                      (i.e., residual deep learning networks for product image
                      analysis) to obtain colour, shapes automatically, and other
                      product features. We validate our expert system and its
                      enhanced knowledge representation with commercial datasets
                      obtained from our ON4OFF research project in a retail case
                      study in the beauty sector.},
      month         = {Sep},
      date          = {2021-09-27},
      organization  = {2021 44th International Convention on
                       Information, Communication and
                       Electronic Technology (MIPRO), Opatija
                       (Croatia), 27 Sep 2021 - 1 Oct 2021},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / EUROCC - National
                      Competence Centres in the framework of EuroHPC (951732) /
                      DEEP-EST - DEEP - Extreme Scale Technologies (754304)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951732 /
                      G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.23919/MIPRO52101.2021.9596796},
      url          = {https://juser.fz-juelich.de/record/902553},
}