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Online service systems have been increasingly popular and important nowadays, with an increasing demand on the availability of services provided by these systems, while significant efforts have been made to strive for keeping services up continuously. Therefore, reducing the MTTR (Mean Time to Restore) of a service remains the most important step to assure the user-perceived availability of the service. To reduce the MTTR, a common practice is to restore the service by identifying and applying an appropriate healing action (i.e., a temporary workaround action such as rebooting a SQL machine). However, manually identifying an appropriate healing action for a given new issue (such as service down) is typically time consuming and error prone. To address this challenge, in this paper, we present an automated mining-based approach for suggesting an appropriate healing action for a given new issue. Our approach generates signatures of an issue from its corresponding transaction logs and then retrieves historical issues from a historical issue repository. Finally, our approach suggests an appropriate healing action by adapting healing actions for the retrieved historical issues. We have implemented a healing suggestion system for our approach and applied it to a real-world product online service that serves millions of online customers globally. The studies on 77 incidents (severe issues) over 3 months showed that our approach can effectively provide appropriate healing actions to reduce the MTTR of the service.