| dc.contributor.author | Gadepally, Vijay | |
| dc.contributor.author | O'Brien, Kyle | |
| dc.contributor.author | Dziedzic, Adam | |
| dc.contributor.author | Elmore, Aaron | |
| dc.contributor.author | Kepner, Jeremy | |
| dc.contributor.author | Madden, Samuel | |
| dc.contributor.author | Mattson, Tim | |
| dc.contributor.author | Rogers, Jennie | |
| dc.contributor.author | She, Zuohao | |
| dc.contributor.author | Stonebraker, Michael | |
| dc.date.accessioned | 2021-11-09T12:57:05Z | |
| dc.date.available | 2021-11-09T12:57:05Z | |
| dc.date.issued | 2017-09 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/137850 | |
| dc.description.abstract | © 2017 IEEE. A polystore system is a database management system composed of integrated heterogeneous database engines and multiple programming languages. By matching data to the storage engine best suited to its needs, complex analytics run faster and flexible storage choices helps improve data organization. BigDAWG (Big Data Working Group) is our prototype implementation of a polystore system. In this paper, we describe the current BigDAWG software release which supports PostgreSQL, Accumulo and SciDB. We describe the overall architecture, API and initial results of applying BigDAWG to the MIMIC II medical dataset. | en_US |
| dc.language.iso | en | |
| dc.publisher | IEEE | en_US |
| dc.relation.isversionof | 10.1109/hpec.2017.8091077 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | arXiv | en_US |
| dc.title | Version 0.1 of the BigDAWG Polystore System | en_US |
| dc.title.alternative | BigDAWG version 0.1 | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Gadepally, Vijay, O'Brien, Kyle, Dziedzic, Adam, Elmore, Aaron, Kepner, Jeremy et al. 2017. "Version 0.1 of the BigDAWG Polystore System." | |
| dc.contributor.department | Lincoln Laboratory | |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
| dc.eprint.version | Original manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2019-06-18T14:12:10Z | |
| dspace.date.submission | 2019-06-18T14:12:11Z | |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
