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In a traditional data warehouse, the “subject oriented” means that all the elements (data attributes) relating to the same real-world event or object are linked together, the elements are classified and navigated by “subject”. But in scientific data warehouse, elements are “faceted classifications oriented”, users want to classify and navigate the elements by mutable classifications flexibly. It is...
There are many new challenges in building Scientific Data Management System (SDMS), and the most initial and important one of them is designing a reasonably and effectively data architecture. The data architecture should focus on massive metadata, complex data flow, multi-classification data, various data productions and uniform query. Based on the applied projects, we present the classificatory criterions,...
This paper is the case study on scientific data cleaning, it proposes some new ideas or applies some hot technologies for scientific data cleaning. There are three challenges of scientific data cleaning tool: domain knowledge representation and usage, customized cleaning flow and building dynamically. We adopt knowledge based rule modeling, workflow based flow modeling and pluggable components based...
The polymorphic, diversified, multi-sources and multi-use scientific data is quite different from the other data, there are many new challenges in building Scientific Data Management and Analysis System (SDMAS), especial one is that the multi-use data requires alternate classifications and mappings between them. Based on the oceanographic data, we present the solutions of multi-classifications problems...
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