Towards Exascale Scientific Metadata Management

TL;DR

This paper proposes an integrated, automated, and standardized metadata management system to enhance scientific data coordination and accelerate innovation across various research domains at exascale computing levels.

Contribution

It introduces a comprehensive approach for automatic, rich metadata capture and storage within datasets, addressing current gaps in domain-agnostic scientific data management.

Findings

Motivates need for systematic metadata management in large-scale science.

Discusses challenges and solutions for metadata integration.

Illustrates applications in plasma physics, climate modeling, neuroscience.

Abstract

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions.