Process Materials Scientific Data for Intelligent Service Using a Dataspace Model

Yang Li; Changjun Hu

doi:10.5334/dsj-2016-007

Process Materials Scientific Data for Intelligent Service Using a Dataspace Model

Authors

Yang Li School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB)
Changjun Hu Beijing Key Laboratory of Knowledge Engineering for Materials Science

DOI:

https://doi.org/10.5334/dsj-2016-007

Keywords:

Intelligent services, Materials Scientific data, Semantic mapping, Big Data, evolutionary algorithm

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.

Author Biographies

Yang Li, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB)

Yang Li, received the Ph.D. degree from University of Science and Technology Beijing, China, in 2010, where she is also received her BSc degree. Her research interests include data integration, semantic web, cloud computing and software engineering.

Changjun Hu, Beijing Key Laboratory of Knowledge Engineering for Materials Science

Changjun Hu, received the Ph.D. degree from Peking University, Beijing, China, in 2001. He is currently a Professor at the School of Information Engineering at the University of Science and Technology Beijing, China. His main research interests include parallel computing, parallel compilation technology, parallel software engineering, network storage system, data engineering and software engineering.

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Published

2016-07-08

Issue

Vol. 15 (2016)

Section

Research Papers

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