EDISON-DATA: A flexible and extensible platform for processing and analysis of computational science data

Abstract

With the recent emergence of new paradigm – open science and big data, the need for data sharing and collaboration is becoming important in the computational science field as well. The EDISON-DATA platform aims to provide services that computational simulation data can easily published, preserved, shared, reused, discovered and analyzed. First, this paper analyzed computational science platform-related issues, obtained during the development of the EDISON-DATA platform, regarding the sharing and reusing of the computational science data. These issues include data complexity, diversity, reliability, heterogeneity, etc. To solve the above issues and support data analysis in an efficient and integrated manner, this study proposes various ideas used in the EDISON-DATA platform. First, we suggested an automated preprocessing framework to handle the complexity of computational science data. Second, to solve the diversity issue, we presented ways to develop preprocessing logics and data presentation logics customized for each data type. Third, to improve the reliability of computational science data, some quality control and provenance management techniques were presented. Fourth, we proposed a way to manage related data in groups. Fifth, to solve data heterogeneity problem and to analyze data in an integrated way, we let the preprocessing framework to use controlled vocabularies to express descriptive metadata. Lastly, we demonstrated feasibility and usability of the proposed ideas in this paper by presenting a case study of building a research portal service in the materials field based on the EDISON-DATA platform.