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

Research output: Contribution to conferencePaperpeer-review

  • External authors:
  • Kyle Chard
  • Mike D' Arcy
  • Ben Heavner
  • Ian Foster
  • Carl Kesselman
  • Ravi Madduri
  • Alexis Rodriguez
  • Kristi Clark
  • Eric W. Deutsch
  • Ivo Dinov
  • Nathan Price
  • Arthur Toga


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.

Bibliographical metadata

Original languageEnglish
Number of pages10
Publication statusPublished - 8 Dec 2016
Event2016 IEEE International Conference on Big Data - Washington, Washington, DC, United States
Event duration: 5 Dec 20168 Dec 2016
Conference number: 2016


Conference2016 IEEE International Conference on Big Data
Abbreviated titleIEEE BigData 2016
Country/TerritoryUnited States
CityWashington, DC
Internet address

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