SSRN Leverages ITX Data Science Team To Reduce Cost, Drive Performance
SSRN’s recent growth ignited rapid expansion of its vast collection of academic research papers. To keep pace with the new submissions, the research repository asked ITX for help modernizing its legacy citation system.
Our data scientists conducted extensive experimentation and testing to devise multiple proofs of concept. We tested citation extraction algorithms against an array of document complexities and key performance measures. We compared data storage solutions to identify the one best able to accommodate a large and expanding collection of citation content.
By applying machine learning algorithms to identify, accurately allocate, and securely store millions of citations automatically – at a fraction of the cost – allowing SSRN to unleash the power of its rich but as yet untapped data set.
- SSRN utilized machine learning algorithms to eliminate their backlog of research papers and accelerate the practice of scholarly research.
- By unleashing the power of the GROBID algorithm, ITX data scientists drove revenue and reduced costs through process automation; and accurately measured, monitored, and improved the research experience for end users.
- The ITX Data Science response continues to power SSRN’s citation extraction and classification system, providing an evergreen solution that continues to deliver value.