|
|
|
|
| LEADER |
01651 am a22002293u 4500 |
| 001 |
124543 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Stonebraker, Michael
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
|e contributor
|
| 100 |
1 |
0 |
|a Lincoln Laboratory
|e contributor
|
| 700 |
1 |
0 |
|a Gadepally, Vijay N.
|e author
|
| 700 |
1 |
0 |
|a Kepner, Jeremy
|e author
|
| 700 |
1 |
0 |
|a Madden, Samuel R.
|e author
|
| 700 |
1 |
0 |
|a Vartak, Manasi
|e author
|
| 245 |
0 |
0 |
|a A demonstration of the BigDAWG polystore system
|
| 260 |
|
|
|b VLDB Endowment,
|c 2020-04-08T17:24:01Z.
|
| 856 |
|
|
|z Get fulltext
|u https://hdl.handle.net/1721.1/124543
|
| 520 |
|
|
|a This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that "one size does not fit all", we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics. ©2015
|
| 546 |
|
|
|a en
|
| 655 |
7 |
|
|a Article
|
| 773 |
|
|
|t 10.14778/2824032.2824098
|
| 773 |
|
|
|t Proceedings of the VLDB Endowment
|
