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

Kyle Chard, Mike D' Arcy, Ben Heavner, Ian Foster, Carl Kesselman, Ravi Madduri, Alexis Rodriguez, Stian Soiland-Reyes, Carole Goble, Kristi Clark, Eric W. Deutsch, Ivo Dinov, Nathan Price, Arthur Toga

    Research output: Contribution to conferencePaperpeer-review

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    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.
    Original languageEnglish
    Number of pages10
    Publication statusPublished - 8 Dec 2016
    Event2016 IEEE International Conference on Big Data - Washington, Washington, DC, United States
    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

    Research Beacons, Institutes and Platforms

    • Institute for Data Science and AI


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