课题基金 / 基金详情

Efficient and Scalable Processing of Dynamic Heterogeneous Graphs

Efficient and Scalable Processing of Dynamic Heterogeneous Graphs
动态异构图的高效且可扩展的处理
批准号:
FT210100303
负责人:
Prof Wenjie Zhang
金额:
$76.79万
依托单位国家:
澳大利亚
项目类别:
ARC Future Fellowships
财政年份:
2022
资助国家:
澳大利亚
项目状态:
未结题
起止时间:
2022-04-30 至 2026-04-29

项目摘要

项目成果

Prof Wenjie Zhang的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
This project aims to develop efficient and scalable algorithms to process large-scale dynamic heterogeneous graphs where graph nodes and edges are of multiple types and the graph structure updates dynamically. Key challenges are expected to be addressed including complex structure, high speed, and large volume of dynamic heterogeneous graphs. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, distributed algorithms as well as a system prototype to demonstrate the practical value. Success of this project will open up a new research direction to enrich frontier technologies and benefit many key applications in Australia including cybersecurity, e-commerce, health and social networks.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Big temporal graph processing in the Cloud
  • 批准号:
    DP230101445
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $34.86万
  • 财政年份:
    2023
  • 负责人:
    Prof Wenjie Zhang
  • 依托单位:
Cohesive Subgraph Discovery on Big Bipartite Graphs
  • 批准号:
    DP200101116
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $30.2万
  • 财政年份:
    2020
  • 负责人:
    Prof Wenjie Zhang
  • 依托单位:
Continuous Loyalty-based Similarity Queries over Moving Objects
  • 批准号:
    DP150103071
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $18.37万
  • 财政年份:
    2015
  • 负责人:
    Prof Wenjie Zhang
  • 依托单位:
Continuously monitoring uncertain objects in a multi-dimensional space
  • 批准号:
    DE120102144
  • 项目类别:
    Discovery Early Career Researcher Award
  • 资助金额:
    $28.61万
  • 财政年份:
    2012
  • 负责人:
    Prof Wenjie Zhang
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis