Abstract: The timely response of network services is crucial to improving user experience. Taking search engine as a typical example of network services, service providers need to ensure that the search response time is within one second. In practice, the search response time can be affected by many service attributes, such as user browsers, ISPs, and page loading methods. To optimize effectively, service providers need to identify the rules that cause high search response time, which are combinations of the above attributes. However, existing work encounters three challenges. First, the amount of search logs is large. Second, the search logs are unevenly distributed. Third, the rules with high generality are needed. Therefore, we propose a framework called Miner (multi-dimensional extraction of rules). Miner takes advantage of self-paced sampling to overcome the first and second challenges. To address the third challenge, Miner employs Corels to generate rules with high generality and recall. Our experiments use search logs provided from two top-tier search engine companies in China. The results show that Miner outperforms the state-of-the-art methods in terms of generality and recall. Operators adopt rules generated by Miner and optimize the performance of the search engine.