Data Evolution Analysis of Virtual DataSpace for Managing the Big Data Lifecycle

New challenge about the constantly changing of associated data in big data management has arisen, which leads to the issue of data evolution. In this paper, a data evolution model of Virtual Data Space (VDS) is proposed for managing the big data lifecycle. Firstly, the concept of data evolution cycle is defined, and the lifecycle process of big data management is described. Based on these, the data evolution lifecycle is analyzed from the data relationship, the user requirements, and the operation behavior. Secondly, the classification and key concepts about the data evolution process are described in detail. According to this, the data evolution model is constructed by defining the related concepts and analyzing the data association in VDS, for the capture and tracking of dynamic data in the data evolution cycle. Then we discuss the cost problem about data dissemination and change. Finally, as the application case, the service process of dynamic data in the field of materials science is described and analyzed. We verify the validity of data evolution modeling in VDS by the comparison of traditional database, data space, and VDS. It shows that this analysis method is efficient for the data evolution processing, and very suitable for the data-intensive application and the real-time dynamic service.