TY - CHAP
T1 - A demonstration of the BigDAWG polystore system
AU - Elmore, A.
AU - Duggan, J.
AU - Stonebraker, M.
AU - Balazinska, M.
AU - Cetintemel, U.
AU - Gadepally, V.
AU - Heer, J.
AU - Howe, B.
AU - Kepner, J.
AU - Kraska, T.
AU - Madden, S.
AU - Maier, D.
AU - Mattson, T.
AU - apadopoulos, S.
AU - Parkhurst, J.
AU - Tatbul, N.
AU - Vartak, M.
AU - Zdonik, S.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84952768318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84952768318&partnerID=8YFLogxK
U2 - 10.14778/2824032.2824098
DO - 10.14778/2824032.2824098
M3 - Chapter
AN - SCOPUS:84952768318
T3 - Proceedings of the VLDB Endowment
SP - 1908
EP - 1911
BT - Proceedings of the VLDB Endowment
PB - Association for Computing Machinery
T2 - 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Y2 - 11 September 2006 through 11 September 2006
ER -