Graph Processing: Oracle Labs Insights

This lecture explores the ubiquity of graphs in modern data and analytics, focusing on the shift in organizations' perception of graph technologies. It delves into representing data as property graphs, RDF graphs, and the transition from relational to property graph models. The lecture discusses the challenges and approaches in distributed graph analytics, emphasizing the performance of Oracle's PGX framework. It covers graph query languages like PGQL and the application of graph machine learning for fraud detection, malware detection, and recommendation systems. Additionally, it showcases the implementation of graph ML techniques, such as vertex embedding and graphlet embeddings, in Oracle Labs' research. The lecture concludes by highlighting the significance of graph processing and Oracle Labs' comprehensive suite for graph analytics.