Database optimization for large-scale web access log management

In this research, we investigate an optimal method of commercial DBMS for analyzing large-scale web access log. Also, we develop the pre-processor in memory structure to improve performance and the user management tool for user friendliness. The optimal method of DBMS for management of large-scale web access log is the key issue in this research. We have three stages for this research. The first stage in research is data collection. In this stage, we study characteristics of large-scale web access log data and investigate commercial DBMS tuning techniques. The next stage, we design the system model. The proposed system has three components including pre-processor, DBMS and management tool. To improve the effectiveness the system, Pre-processor hash and sort log data in memory. And we perform DBMS tuning to improve performance. The final stage, we implement system and evaluate the performance. And we develop system management tool for user friendliness. The research output of this project can be divided into analysis of large-scale DBMS, DBMS tuning and system management software. From output, we provide DBMS tuning techniques for large-scale data log. Also it is expected to use of collected data and analyzed information in other area such as network security.