SBIR Phase I: Artificial Intelligence (AI) System for Enterprise Software Incident Management

SBIR 第一阶段：用于企业软件事件管理的人工智能 (AI) 系统

• 批准号: 2052608
• 负责人: Helen Gu
• 金额: $ 25.6万
• 依托单位: InsightFinder Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052608&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; AI

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be improving the robustness and performance of enterprise information infrastructure. Automated solutions for incident management are critical. The proposed project develops an automated artificial intelligence (AI) system to interpret reports of IT problems and recommend solutions. This Small Business Innovation Research (SBIR) Phase I project aims at transforming IT incident management for enterprise companies. The novelty of the proposed solution lies in two unique features: 1) intelligent learning techniques that can automatically infer effective remediation actions without requiring tedious and error-prone user-defined rules; and 2) active learning capabilities which can adaptively refine the prevention actions based on continuous system health monitoring data and user feedback. Specifically, the proposed project consists of three thrusts: 1) building universal pattern extraction schemes which can extract precise and reusable patterns from various data sources including complex incident ticket data, metric data, and log data; 2) exploring a self-learning remediation recommendation system that can recommend proper remediation by analyzing both the incident pattern and root cause patterns; and 3) easy-to-use remediation workflow engine which allows the user to trigger different remediation workflows to prevent the predicted incident in one place.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将提高企业信息基础设施的鲁棒性和性能。事件管理的自动化解决方案至关重要。拟议项目开发一个自动化人工智能（AI）系统，以解释IT问题的报告并推荐解决方案。 这个小型企业创新研究（SBIR）第一阶段项目旨在为企业公司转变IT事件管理。所提出的解决方案的新奇在于两个独特的功能：1）智能学习技术，可以自动推断有效的补救措施，而不需要繁琐和容易出错的用户定义的规则;和2）主动学习能力，可以自适应地完善预防措施的基础上连续的系统健康监测数据和用户反馈。具体而言，该项目包括三个重点：1）建立通用的模式提取方案，可以从各种数据源，包括复杂的事件票据数据，度量数据和日志数据中提取精确和可重用的模式; 2）探索一个自学习的补救推荐系统，可以通过分析事件模式和根本原因模式来推荐适当的补救;以及3）易于使用的补救工作流程引擎，允许用户触发不同的补救工作流程，以防止在一个地方发生预测的事故。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。