|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||Cloud-based Infrastructure for Big Data in the Materials Domain
||David C. Elbert, Nick Carey, Tamas Budavari, Gerard Lemson, Alex Szalay, Tyrel McQueen, K.T. Ramesh
|On-Site Speaker (Planned)
||David C. Elbert
Materials science and engineering faces Big Data challenges from advances in instrumentation and computational modeling. In two projects we use SciServer for cloud-based, materials-specific infrastructure to provide data curation, visualization, and analysis to diverse, materials-domain workgroups.
In the MEDE Data Science Cloud (MEDE-DSC), SciServer provides data-centric computing infrastructure with collaborative integration into the materials design loop. Shared data are accessible from local, containerized computational tools using a web based, Jupyter frontend. Version-controlled containers and notebooks bring power, consistency and transparency while moving towards reproducible, narrated computation. RESTful APIs provide integration to other MGI resources.
In the PARADIM Data Collective (PDC) SciServer provides a data-driven, collaborative discovery platform with data browsing, federation, and analysis. PDC infrastructure includes event-triggered data ingress, metadata harvesting, and processing. PDC provides data browsing, visualization and compute tools in a JupyterLab environment. PDC integrates Machine Learning to the data environment to optimize opportunities across experiments.
||Planned: Supplemental Proceedings volume