Extracting Information from Large Scale Graph Data: Case Study on Automated UI Testing - Oguz, Ramazan Faruk; Oz, Mert; Olmezogullari, Erdi; Aktas, Mehmet Siddik (SPRINGER INTERNATIONAL PUBLISHING AG, 2022)
Even though a large-scale graph structure is a powerful model to solve several challenging problems in various applications' domains today, it can also preserve various raw essences regarding user behavior, especially in the e-commerce domain. Information extraction is a promising research area in deep learning algorithms using large-scale graph data. This study focuses on understanding users' implicit navigational behavior on an e-commerce site that we can represent with the large-scale graph data. We propose a GAN-based e-business workflow by leveraging the large-scale browsing graph data and the footprints of navigational users' behavior on the e-commerce site. With this method, we have discovered various frequently repeated click-stream data sequences, which do not appear in training data at all. Therefore, We developed a prototype application to demonstrate performance tests on the proposed business e-workflow. The experimental studies we conducted show that the proposed methodology produces noticeable and reasonable outcomes for our prototype application.
Keywords: Deep learning; Large scale graph data; GAN; Distributed e-business workflows; Distributed systems; Computer Science