Traceability and Provenance in Big Data Medical Systems

TL;DR

This paper discusses a provenance tracking approach in big data medical systems, enabling detailed data and process traceability to support clinical research, exemplified through projects on Alzheimer's disease.

Contribution

It introduces an adaptable provenance management method using CRISTAL, tailored for large-scale medical data analysis in neuroscience research.

Findings

Enhanced traceability in neuGRID and N4U projects

Support for complex, large-scale biomedical data analyses

Improved management of data and workflow evolution

Abstract

Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimers disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.