I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets

Conference · Fri Jan 01 00:00:00 EST 2016

DOI:https://doi.org/10.1109/BigData.2016.7840618· OSTI ID:1394620

Chard, Kyle; Arcy, Mike; Heavner, Benjamin D.; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Rodriguez, Alexis; Soiland-Reyes, Stian; Goble, Carole

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing. We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: National Institutes of Health (NIH); USDOE Office of Science (SC)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1394620
• Resource Relation: Conference: 2016 IEEE International Conference on Big Data (Big Data), 12/05/16 - 12/08/16, Bethesda, MD
• Country of Publication: United States
• Language: English