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

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）已经成为所谓的企业计算的核心。存储子系统日益成为关键子部件和单点故障。在涉及多个供应商、机器、软件产品、拓扑结构和文化（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。需要一个更复杂的工具来积累知识，处理复杂性和变化的能力，与不同的知识库交互的能力，并根据经验和反馈预测解决方案的概率。通过开发自然语言接口的多语言支持和功能将提供适合当今全球业务中管理企业数据的功能。

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.