On the Machine Learning Based Business Workflows Extracting Knowledge from Large Scale Graph Data

Abstract

AbstractThe data created by web users while navigating on a website constitutes graph data. Large-scale graph data is generated on websites many users visit with high frequency. Analyzing large-scale graph data using artificial intelligence techniques and predicting user behavior by creating models is an actively studied research topic. Within the scope of this research, a machine learning business process is proposed that will allow the interpretation of graph data obtained from web user navigation data. A prototype application was developed to demonstrate the usability of the proposed business process. The developed prototype application was run on graph data obtained from websites with intense user-system interaction. A comprehensive evaluation study was carried out on the prototype application. The results obtained from the empirical evaluation are promising and show that the proposed business process is used.KeywordsMachine learningSequential pattern miningCustomer journeyFunnel analysisEncoding