课题基金 / 基金详情

Designing a Scalable and Robust Infrastructure for Highly Dynamic Web Services

Designing a Scalable and Robust Infrastructure for Highly Dynamic Web Services
为高度动态的 Web 服务设计可扩展且稳健的基础架构
批准号:
LP0347217
负责人:
Prof Zahir Tari
金额:
$18.41万
依托单位:
依托单位国家:
澳大利亚
项目类别:
Linkage Projects
财政年份:
2003
资助国家:
澳大利亚
项目状态:
已结题
起止时间:
2003-11-05 至 2007-09-30

项目摘要

项目成果

Prof Zahir Tari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
Web services are modular Internet-based applications that communicate with one another over the Internet. However, because of the high dynamic nature of the Internet, existing web services have profound limitations as they lack the support of semantic-based service discovery and dynamic service composition. This project aims at designing a robust and scalable infrastructure to support highly dynamic web services. This infrastructure will provide an adaptive e-business platform enabling optimal integration of enterprise' complex business processes across external systems and third party applications. The outcomes will be a set of advanced techniques and algorithms that will form the basis of the next generation of e-business solutions.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Secure Management of Internet of Things Data for Critical Surveillance
  • 批准号:
    LP230100276
  • 项目类别:
    Linkage Projects
  • 资助金额:
    $28.45万
  • 财政年份:
    2024
  • 负责人:
    Prof Zahir Tari
  • 依托单位:
Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares
  • 批准号:
    DP240101032
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $35.49万
  • 财政年份:
    2024
  • 负责人:
    Prof Zahir Tari
  • 依托单位:
Scalable Stream Processing in Hybrid Edge-Cloud Infrastructures
  • 批准号:
    DP230100260
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $31.71万
  • 财政年份:
    2023
  • 负责人:
    Prof Zahir Tari
  • 依托单位:
Resource Allocation for High-Volume Streaming Data in Data Centers
  • 批准号:
    DP200100005
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $26.33万
  • 财政年份:
    2020
  • 负责人:
    Prof Zahir Tari
  • 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