Stream2Graph: Dynamic Knowledge Graph for Online Learning Applied in Large-scale Network

Knowledge Graphs (KG) are valuable information sources that store knowledge in a domain (healthcare, finance, e-commerce, cyber-security.). Most industrial KGs are dynamic by nature as they are updated regularly with streaming data (customer activity, network traffic, application logs, IT process). However, extracting insights from continuously updated data comes with major challenges, particularly in big data settings. In this paper, we address the following challenges: 1) ingesting heterogeneous data, 2) training and deployment of predictive models on continuously evolving data, and 3) implementation of data pipelines for updating and maintaining the KG in production. We cover multiple aspects of this process, from knowledge collection to its operationalization. We propose Stream2Graph, a stream-based system for building and updating the knowledge base dynamically in real time. Then we show how graph features can be used in downstream online machine learning models. The solution speeds up big data stream learning and knowledge extraction to enhance Graph-based AI applications. Experimental results show the effectiveness of our solution for knowledge base construction and improvement of big data learning capabilities. Using data from Stream2Graph resulted in speedups for training and inference time in the range from 547x to 2000x in downstream ML models. Finally, we provide the lessons learned from applying graph-based online learning on large-scale network processing high-velocity streaming data.

I. Introduction

Multiple industrial information sources, including sensor networks, financial markets, social networks, healthcare monitoring, and AIOps, are represented as streaming data that need to be processed by online machine learning methods [1].