Extracting Information from Large Scale Graph Data: Case Study on Automated UI Testing

Oguz R. F., Oz M., Olmezogullari E., AKTAŞ M. S.

27th International European Conference on Parallel and Distributed Computing (Euro-Par), ELECTR NETWORK, 30 Ağustos - 03 Eylül 2021, cilt.13098, ss.364-375 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 13098
  • Doi Numarası: 10.1007/978-3-031-06156-1_29
  • Basıldığı Ülke: ELECTR NETWORK
  • Sayfa Sayıları: ss.364-375
  • Anahtar Kelimeler: Deep learning, Large scale graph data, GAN, Distributed e-business workflows, Distributed systems
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.