Mass Data Management Of Digitized Virtual Human Based On Grid

With the quick development of the information technology, data explosion has become a challenging problem, which leads to that mass data management technology becomes a new focus and hotspot in the research area. With the Digitized Virtual Human as background, this thesis put emphasis on mass data management technologies.In this dissertation, the Data Grid idea was introduced into the Digitized Virtual Human research. Taking the characteristics of Digitized Human datasets into consideration, some solutions for key technologies of mass data management were presented.The main contributions of the dissertation are summarized as follows:â‘ Research on Data Grid metadata modelMetadata is one of the most important components of the data management. Most existing researches on Grid metadata have deficiency. Consequently, a Data Grid metadata model was put forward in this dissertation. Through a semantic-hierarchy blended model, the metadata model realized scalability and load balance for data management. All information of the required data may be directed to related location, which improved the inquiry efficiency greatly.â‘¡Research on replication management strategyData Grid provides geographically distributed data resources, ensuring efficient and fast access to such huge and widely distributed data, however, is hindered by the high latency of the Internet. Replication is an effective approach to address these problems. We discussed the granularity and creating time of replica, proposing replica granularity selection model and replica creating time decision strategy respectively, which provides feasible approach to replication management.â‘¢System analysis and design on mass data management based on GridBased on Grid technology, we propose design principles and functions for mass data management system, coupled with the analysis of demands of the system. Furthermore, a system structure for Digitized Virtual Human was proposed and system characteristics were summarized. At last, summaries of the previous work are given, and future research plan is pointed out.