Big data management challenges in health research—a literature review. (7th August 2017)

• Record Type: Journal Article
• Authors: Wang, Xiaoming; Williams, Carolyn; Liu, Zhen Hua; Croghan, Joe
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 1(2019)
• Page Start: 156
• Page End: 167
• Publication Date: 2017-08-07
• Subjects: big data management -- system performance -- data quality -- machine learning -- SQL and NoSQL; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbx086
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.