MySQL database is more and more widely used, and the problems of data recovery and forensics brought about by it have become more and more people's attention. The current common method of MySQL data recovery is to analyze the redo log. However, the redo log is based on the InnodDB storage engine. The storage engine in MySQL is not only InnoDB, so this method is not universal. In MySQL, there is also a very important log, which is a binary log with high efficiency and flexibility. This article analyzes the structure of MySQL binary log and uses binlog to restore the database by studying the principle of MySQL binary log function. This article analyzes and compares the mysqlbinlog tool and its defects, and uses Python to write a better binlog analysis tool to better adapt and generate standard SQL, and has a rollback function to recover data better. Use new tools to prove its practical significance and conduct post-research on forensics.

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The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a trianglebased quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both nonoverlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.