Tiered data storage addressed in HPCWire White Paper

University of Miami Center for Computational Science researchers published a White Paper in the latest issue of HPCWire on its unique tiered data storage system designed to address the burgeoning amounts of research data that address some of the world’s biggest and most complex issues.
Data in Motion – A New Paradigm in Research Data Lifecycle Management presents the complex set of interrelated challenges to managing ever-growing data with an integrated system that factors in data accessibility, interoperability, expansion and flexibility.

“We live in a world rich with data. Whether we are looking at climate change, genomics or historical economic trends, data volume has grown and continues to grow exponentially,” said Nick Tsinoremas, the Center director and one of the paper’s authors. “In today’s world of scientific discovery, finding a way to manage the layers of data in their various forms and formats presents new and continuing challenges.”

The Center has collaborated with experts across the University and around the globe, exploiting supercomputers that can perform trillions of calculations per second. To be a relevant part of research teams and ensure that scientific advances can proceed in a timely fashion, it has developed a novel, yet simply designed four-tiered data storage and management approach. The paper published in HPCWire presents many issues that make up the complex set of interrelated challenges to managing this tsunami of data with an integrated data management system.

“Science advances today hinge on our ability to successfully address these issues of data accessibility and interoperability,” said Joel Zysman, director of high-performance computing and another of the paper’s authors. “This provides better awareness of existing databases, and resolving maintenance responsibility and ownership uncertainty. Ultimately, we believe that solutions like this one must recognize the need for expansion and flexibility so anyone who can bring insight to these data – now or in the future – can do so and exponentially advance science and other data-driven fields.”