Schema-independent scientific data cataloging framework

Modern scientific experiments generate vast volumes of data which are hard to keep track of. Consequently, scientists find it difficult to reuse and share these data sets. We address this problem by developing a schema-independent data cataloging framework for efficient management of scientific data. The proposed solution consists of an agent which automatically identifies new data products and extract metadata from them, as well as a server which indexes the metadata using a NoSQL database and provides a REST API for querying, sharing, and reusing the data sets. The novelty of our solution lies in the pluggable metadata extraction logic, extensible data product generation monitors, use of a NoSQL database, and the ability to dynamically add new metadata fields. The use of Apache Solr as the backend database enables the proposed solution to index and search data products much faster than a solution based on relational databases. For example, our Apache Solr based implementation can resolve full text, sub-string, prefix, and suffix queries 91 %-99 % faster than a MySQL-based implementation.