课题基金 / 基金详情

Domain-Driven Actionable Link Discovery

Domain-Driven Actionable Link Discovery
域驱动的可操作链接发现
批准号:
DP0773412
负责人:
Prof Longbing Cao
金额:
$15.62万
依托单位国家:
澳大利亚
项目类别:
Discovery Projects
财政年份:
2007
资助国家:
澳大利亚
项目状态:
已结题
起止时间:
2007-02-01 至 2010-06-30

项目摘要

项目成果

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中文摘要
翻译
在诸如检测恐怖分子和分布式欺诈活动等紧急数据挖掘应用的驱动下,该项目将提供有效的技术和算法,用于发现分散在相关企业信息系统中的孤立但重要的链接。这些成果将使澳大利亚能够在新的链路发现领域发挥重要作用。他们将赋予澳大利亚组织强大的数据挖掘能力,以处理关键的跨组织问题,例如反恐,跨市场监视,审计监视和社交网络分析。他们将通过智能信息的使用来提高澳大利亚的社会保障、合规性和生活质量。
英文摘要
Driven by emergent data mining applications such as detecting terrorist and distributed fraudulent activities, this project will deliver effective techniques and algorithms for discovering isolated yet important links dispersed across related enterprise information systems. The outcomes will enable Australia to play a significant role in the new link discovery area. They will empower Australian organizations with powerful data mining capabilities to deal with critical cross-organization problems, for instance, counter-terrorism, cross-market surveillance, auditing surveillance and social network analysis. They will enhance Australian social security, compliance and life quality through smart information use.
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Federated Omniverse Facilities for Smart Digital Futures
  • 批准号:
    LE240100131
  • 项目类别:
    Linkage Infrastructure, Equipment and Facilities
  • 资助金额:
    $35.94万
  • 财政年份:
    2024
  • 负责人:
    Prof Longbing Cao
  • 依托单位:
Data Complexity and Uncertainty-Resilient Deep Variational Learning
  • 批准号:
    DP240102050
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $31.25万
  • 财政年份:
    2024
  • 负责人:
    Prof Longbing Cao
  • 依托单位:
Deep Interaction Learning in Unlabelled Big Data and Complex Systems
  • 批准号:
    FT190100734
  • 项目类别:
    ARC Future Fellowships
  • 资助金额:
    $71.32万
  • 财政年份:
    2020
  • 负责人:
    Prof Longbing Cao
  • 依托单位:
Deep analytics of non-occurring but important behaviours
  • 批准号:
    DP190101079
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $22.49万
  • 财政年份:
    2019
  • 负责人:
    Prof Longbing Cao
  • 依托单位:
国内基金
海外基金
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information