I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets
Research output: Contribution to conference › Paper
Abstract
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 language | English |
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Number of pages | 10 |
DOIs | |
State | Published - 8 Dec 2016 |
Event | 2016 IEEE International Conference on Big Data - Washington, Washington, DC, United States Event duration: 5 Dec 2016 → 8 Dec 2016 Conference number: 2016 http://cci.drexel.edu/bigdata/bigdata2016/ |
Conference
Conference | 2016 IEEE International Conference on Big Data |
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Abbreviated title | IEEE BigData 2016 |
Country | United States |
City | Washington, DC |
Period | 5/12/16 → 8/12/16 |
Internet address |
Related information
Publications
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article