Efficient and Scalable Similarity Query Processing on Big Streaming Graphs

该项目旨在为大型流图开发高效和可扩展的相似度查询的新方法，大型流图是大型图，其中图节点和边可能以高速到达或过期。预计将解决三个关键挑战，包括高速、多种类和大量流图。预期成果包括新的理论、新的索引和查询处理技术、先进的分布式算法以及用于评估和展示实用价值的系统原型。这个项目的成功将为许多重要的应用带来显著的好处，比如电子商务、网络安全、健康、社交网络和生物信息学。

This project aims to develop novel approaches for efficient and scalable similarity queries on big streaming graphs which are large-scale graphs where graph nodes and edges may arrive or expire at high speed. Three key challenges are expected to be addressed including high speed, large variety, and big volume of streaming graphs. Expected outcomes include new theories, novel indexing and query processing techniques, and advanced distributed algorithms 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 e-commerce, cybersecurity, health, social networks, and bio-informatics.