Advanced distributed data integration infrastructure and research data management portal

The amount of data available due to the rapid spread of advanced information technology is exploding. At the same time, continued research on data integration systems aims to provide users with uniform data access and efficient data sharing. The ability to share data is particularly important for interdisciplinary research, where a comprehensive picture of the subject requires large amounts of data from disparate data sources from a variety of disciplines. While there are numerous data sets available from various groups worldwide, the existing data sources are principally oriented toward regional comparative efforts rather than global applications. They vary widely both in content and format. Such data sources cannot be easily integrated, and maintained by small groups of developers. I propose an advanced infrastructure for large-scale data integration based on crowdsourcing. In particular, I propose a novel architecture and algorithms to efficiently store dynamically incoming heterogeneous datasets enabling both data integration and data autonomy. My proposed infrastructure combines machine learning algorithms and human expertise to perform efficient schema alignment and maintain relationships between the datasets. It provides efficient data exploration functionality without requiring users to write complex queries, as well as performs approximate information fusion when exact match does not exist. Finally, I introduce Col*Fusion system that implements the proposed advance data integration infrastructure