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000902553 0247_ $$2doi$$a10.23919/MIPRO52101.2021.9596796
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000902553 037__ $$aFZJ-2021-04354
000902553 1001_ $$0P:(DE-Juel1)178934$$aBarakat, Chadi$$b0$$eCorresponding author$$ufzj
000902553 1112_ $$a2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)$$cOpatija$$d2021-09-27 - 2021-10-01$$wCroatia
000902553 245__ $$aDesign and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with Parallel Association Rule Mining and Scalable Recommenders
000902553 260__ $$c2021
000902553 300__ $$a274 - 279
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000902553 520__ $$aGiven 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.
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000902553 536__ $$0G:(EU-Grant)951732$$aEUROCC - National Competence Centres in the framework of EuroHPC (951732)$$c951732$$fH2020-JTI-EuroHPC-2019-2$$x1
000902553 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x2
000902553 588__ $$aDataset connected to CrossRef Conference
000902553 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b1$$ufzj
000902553 7001_ $$0P:(DE-HGF)0$$aBrynjolfsson, S.$$b2
000902553 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b3$$ufzj
000902553 7001_ $$0P:(DE-Juel1)185652$$aBusch, Josefine$$b4$$ufzj
000902553 7001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b5$$ufzj
000902553 773__ $$a10.23919/MIPRO52101.2021.9596796
000902553 8564_ $$uhttps://juser.fz-juelich.de/record/902553/files/HPC-Based%20Retail%20Data%20Analysis%20-%20Author%20Fulltext.pdf$$yOpenAccess$$zStatID:(DE-HGF)0510
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000902553 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178695$$aForschungszentrum Jülich$$b5$$kFZJ
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000902553 9141_ $$y2021
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