Achieving higher availability of storage subsystems through application of a self learning expert system

通过应用自学习专家系统实现存储子系统的更高可用性

• 批准号: LP0347995
• 负责人: Prof Deborah Richards
• 金额: $ 3.51万
• 依托单位: Macquarie University
• 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
• 起止时间: 2003-07-25 至 2005-07-25
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP0347995
• 关键词: Achieving; higher; availability; storage; subsystems

In todays global business environment the management, storage and security of enterprise data (data unavailability, data loss and corruption, systems performance) has become the heart of so-called Enterprise computing. The storage subsystems increasingly have become the critical subcomponent and single point of failure. Discovering the cause of failure in complex environments involving multiple vendors, machines, software products, topologies and cultures (languages) is in many cases time consuming and difficult resulting in unacceptable systems downtime and high maintenance costs. A more sophisticated tool is needed allowing the accumulation of knowledge, the ability to deal with complexity and change, the ability to interface with unlike knowledge bases and predict solution probability based on experience and feedback. Multi-lingual support and capability through the development of a Natural Language interface would provide a functional capability suited to managing enterprise data in todays global businesses.

在当今的全球商业环境中，企业数据的管理、存储和安全（数据不可用、数据丢失和损坏、系统性能）已成为所谓企业计算的核心。 存储子系统日益成为关键的子组件和单点故障。 在涉及多个供应商、机器、软件产品、拓扑和文化（语言）的复杂环境中发现故障原因在许多情况下既耗时又困难，导致不可接受的系统停机时间和高昂的维护成本。 需要一种更复杂的工具来积累知识、处理复杂性和变化的能力、与不同知识库交互的能力以及根据经验和反馈预测解决方案概率的能力。通过开发自然语言界面提供的多语言支持和功能将提供适合管理当今全球业务中的企业数据的功能。