亚太知识发现和数据挖掘会议(PAKDD)是数据挖掘和知识发现领域成立时间最长、最具领导地位的国际会议之一。它为研究人员和行业从业者提供了一个国际论坛,以分享他们来自所有KDD相关领域的新思想、原始研究成果和实际开发经验,包括数据挖掘、数据仓库、机器学习、人工智能、数据库、统计、知识工程、可视化、决策系统和新兴应用程序。 官网地址:http://dblp.uni-trier.de/db/conf/pakdd/

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In the data center, unexpected downtime caused by memory failures can lead to a decline in the stability of the server and even the entire information technology infrastructure, which harms the business. Therefore, whether the memory failure can be accurately predicted in advance has become one of the most important issues to be studied in the data center. However, for the memory failure prediction in the production system, it is necessary to solve technical problems such as huge data noise and extreme imbalance between positive and negative samples, and at the same time ensure the long-term stability of the algorithm. This paper compares and summarizes some commonly used skills and the improvement they can bring. The single model we proposed won the top 14th in the 2nd Alibaba Cloud AIOps Competition belonging to the 25th PAKDD conference. It takes only 30 minutes to pass the online test, while most of the other contestants' solution need more than 3 hours. Codes has been open source to https://www.github.com/ycd2016/acaioc2.

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