A secured big-data sharing platform for materials genome engineering: State-of-the-art, challenges and architecture

【Author】 Wang, Ran; Xu, Cheng; Dong, Runshi; Luo, Zhenghui; Zheng, Rong; Zhang, Xiaotong

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【Abstract】Materials are the foundation of social development. The vigorous development of big-data technology has brought new opportunities for material research and development, gradually entering the data -driven paradigm. How to safely collect, store and utilize material big-data to realize the design and prediction of advanced materials has essential research significance and value. Many material big-data platforms have been constructed to gather multi-source heterogeneous material data. However, these traditional platforms are hard to realize the safe and efficient circulation and utilization of data. Relying on the national Materials Genome Engineering (MGE) project, we built a secured big-data sharing platform and proposed corresponding data collection, storage, utilization, and security solutions. On the one hand, the blockchain framework working as a 'middleware' provides a standard application program interface for data interaction between participants, and participants do not need to perceive the underlying system framework; on the other hand, it provides a unified management and security mechanism for the platform. In terms of collection, the dynamic container model is used to solve the data normalization problem, thereby improving data quality. In terms of storage, data adaptors store normalized data in different databases for distributed storage and unified scheduling. The platform provides a unified service gateway to schedule all services for data utilization. The secured big-data sharing platform can improve data utilization, promote material data sharing, accelerate material discovery, and serve the data needs of high-throughput computing and the design of new materials. (c) 2022 Elsevier B.V. All rights reserved.

【Keywords】Materials genome engineering; Big data; Data sharing; Blockchain; Merkle Patricia Tree; Secure multi-party computation

【发表时间】2023 MAY

【收录时间】2023-02-19

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-数据管理