Online service systems have been increasingly popular and im-portant 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. To assure the user-perceived availability of a service, reducing the Mean Time To Restore (MTTR) of the service remains a very important step. 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 re-booting a SQL machine). However, manually identifying an ap-propriate 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 min-ing-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 im-plemented a healing-suggestion system for our approach and ap-plied it to a real-world online service system that serves millions of online customers globally. The studies on 77 incidents (severe issues) over three months showed that our approach can effec-tively provide appropriate healing actions to reduce the MTTR of the service.