The BigDAWG Polystore System and Architecture
Name
1609.07548.pdf
Description
Accepted version
Size
373.21 KB
Format
Adobe PDF
Checksum (MD5)
a4a7c4b73b32e008955d659f2f146723
Author(s)
Gadepally, Vijay
Chen, Peinan
Duggan, Jennie
Elmore, Aaron
Haynes, Brandon
Kepner, Jeremy
Madden, Samuel
Mattson, Tim
Stonebraker, Michael
Date Issued
September 2016
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Gadepally, Vijay, Chen, Peinan, Duggan, Jennie, Elmore, Aaron, Haynes, Brandon et al. 2016. "The BigDAWG Polystore System and Architecture."
Version
Author's final manuscript
Abstract
© 2016 IEEE. Organizations are often faced with the challenge of providing data management solutions for large, heterogenous datasets that may have different underlying data and programming models. For example, a medical dataset may have unstructured text, relational data, time series waveforms and imagery. Trying to fit such datasets in a single data management system can have adverse performance and efficiency effects. As a part of the Intel Science and Technology Center on Big Data, we are developing a polystore system designed for such problems. BigDAWG (short for the Big Data Analytics Working Group) is a polystore system designed to work on complex problems that naturally span across different processing or storage engines. BigDAWG provides an architecture that supports diverse database systems working with different data models, support for the competing notions of location transparency and semantic completeness via islands and a middleware that provides a uniform multi-island interface. Initial results from a prototype of the BigDAWG system applied to a medical dataset validate polystore concepts. In this article, we will describe polystore databases, the current BigDAWG architecture and its application on the MIMIC II medical dataset, initial performance results and our future development plans.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Lincoln Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/hpec.2016.7761636
