Towards Processing of Big Streaming Temporal Graphs

该项目旨在开发高效、可扩展的算法来处理大流时态图，这是许多数据密集型应用的高需求，如网络安全、犯罪监控和网络营销。特别是，我将研究三种最具代表性的查询类型，包括基于顶点的查询、基于路径的查询和基于子图的查询。该项目的预期成果包括处理大流时态图的理论基础和可扩展算法，以及用于评估和展示其实用价值的系统原型。这个项目的成功应该会给许多重要的应用带来显著的好处，如网络安全、电子商务、健康和社会分析。

This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big streaming temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and social analysis.