Design 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

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.