Efficient and Scalable Subgraph Search from Big Graphs in Cloud

从云中的大图进行高效且可扩展的子图搜索

• 批准号: DP160101513
• 负责人: Prof Lu Qin
• 金额: $ 23.48万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160101513
• 关键词: Efficient; Scalable; Subgraph; Search; Big

This project seeks to develop knowledge that will form the foundation for next-generation graph search engines of structural knowledge. Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection and sales pattern discovery. However, there are many challenges when the search needs to be conducted in cloud environments. This project aims to develop efficient and scalable query processing algorithms to search subgraphs from a big graph stored in a cluster of machines in the cloud. The expected outcomes may lay the theoretical foundations for subgraph search in the cloud, and establish a unified graph search engine that integrates various query semantics for use in many applications.

该项目旨在发展知识，这将构成结构知识的下一代图搜索引擎的基础。子图搜索对许多涉及大图的应用程序的需求很高，例如社交网络营销，犯罪检测和销售模式发现。但是，当需要在云环境中进行搜索时，存在许多挑战。该项目旨在开发高效且可扩展的查询处理算法，以从存储在云中的一组机器中的大图中搜索子图。预期的结果可能是在云中为子图搜索的理论基础，并建立一个统一的图形搜索引擎，该引擎集成了各种查询语义，以用于许多应用程序。