Abstract
Nowadays, materials scientific data come from lab experiments, simulations, individual archives, enterprise and internet in all scales and formats. The data flood has outpaced our capability to process, manage, analyze, and provide intelligent services. Extracting valuable information from the huge data ocean is necessary for improving the quality of domain services. The most acute information management challenges today stem from organizations relying on amounts of diverse, interrelated data sources, but having no way to manage the dataspaces in an integrated, user-demand driven and services convenient way. Thus, we proposed the model of Virtual DataSpace (VDS) in materials science field to organize multi-source and heterogeneous data resources and offer services on the data in place without losing context information. First, the concept and theoretical analysis are described for the model. Then the methods for construction of the model is proposed based on users’ interests. Furthermore, the dynamic evolution algorithm of VDS is analyzed using the user feedback mechanism. Finally, we showed its efficiency for intelligent, real-time, on-demand services in the field of materials engineering.
Highlights
As many industries and research labs handle increasing amount of data in materials science, big data (Toffler 1980) is being considered as an important issue for materials engineering services
The technical characteristics of Virtual DataSpace (VDS) could well satisfy the needs of big data processing and intelligent service
For the VDS proposed in this paper, we developed a “Materials Scientific Data Sharing Service Platform” based on the construction of a Materials Virtual DataSpace (MatVDS) to implement intelligent service applications in the field of materials engineering
Summary
As many industries and research labs handle increasing amount of data in materials science, big data (Toffler 1980) is being considered as an important issue for materials engineering services. Reuse and collaboration in various sources pose new challenges to the field of materials science (Howe et al 2008; Lynch 2008). Materials scientific data comes from lab experiments, simulations, individual archives, enterprise and internet in all scales and formats. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Materials science data possess the typical characteristics of big data
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