As the users continuously play the mobile game, the mobile game service requires a method to manage their information such as their game play score and their own game items. For the purpose of responding to the user requests in real time, the proposed method reduces the size of the database by separating the information of inactive users from the main database. Considering the construction cost, the proposed method moves the information of the inactive users to the file instead of another secondary database system. In order to minimize the effect of the system environment such as CPU or memory, the proposed method depends on the response time to the user request rather than the last login time or the number of the user accounts. For the safety and consistency of the database, the proposed method utilizes a traditional RDBMS rather than NoSQL.

Attempts to hack online game servers and abusing problems in online games have been issues in game industry. Mobile games are famous these days thanks to the widespread of smart devices. Unfortunately, mobile games have very short life cycle. Therefore, analysis of game log data becomes more important to overcome the hacking and abusing problems in online games and extend their life cycles in mobile games. In this paper, we propose a new game log data analysis technique based on the MapReduce methodology. MapReduce is a widely used programming model for analyzing and processing Big data. Instead of processing each analysis query separately, the proposed technique processes multiple analysis queries together in a batch by a single, optimized MapReduce job. As a result, the number of queries processed per unit time increases significantly. Experiment results show that the proposed technique improves the performance significantly compared to a naive method.