子组发现如何帮助AIOps？

摘要：

现代IT系统的真正监管带来了新的挑战，因为在分析和监控大数据流时，它需要更高的可扩展性，可靠性和效率标准。基于规则的推理机是检测异常和自动解决异常的维护系统的关键组成部分。然而，它们仍然局限于简单和一般的规则，不能处理大量的数据，也不能处理IT系统发出的大量警报，这是从专家系统时代吸取的教训。操作系统人工智能（AIOps）提出利用大数据的高级分析和机器学习来改进和自动化监督系统的每一步，并帮助事件管理检测中断，确定根本原因并采取适当的修复措施。然而，最好的AIOps技术依赖于不透明的模型，这极大地限制了它们的采用。作为博士论文的一部分，我们研究了子组发现如何帮助AIOps。这种有前途的数据采掘技术提供了从数据中提取有趣的假设和理解预测模型背后的潜在过程的可能性。为了确保我们的命题的相关性，这个项目涉及数据采掘的研究人员和从业人员从Infologic，法国软件编辑。

英文摘要：

The genuine supervision of modern IT systems brings new challenges as it requires higher standards of scalability, reliability and efficiency when analysing and monitoring big data streams. Rule-based inference engines are a key component of maintenance systems in detecting anomalies and automating their resolution. However, they remain confined to simple and general rules and cannot handle the huge amount of data, nor the large number of alerts raised by IT systems, a lesson learned from expert systems era. Artificial Intelligence for Operation Systems (AIOps) proposes to take advantage of advanced analytics and machine learning on big data to improve and automate every step of supervision systems and aid incident management in detecting outages, identifying root causes and applying appropriate healing actions. Nevertheless, the best AIOps techniques rely on opaque models, strongly limiting their adoption. As a part of this PhD thesis, we study how Subgroup Discovery can help AIOps. This promising data mining technique offers possibilities to extract interesting hypothesis from data and understand the underlying process behind predictive models. To ensure relevancy of our propositions, this project involves both data mining researchers and practitioners from Infologic, a French software editor.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。