TY - GEN
T1 - SEEP
T2 - Middleware'10 Posters and Demos Track, Middleware Posters'10
AU - Migliavacca, Matteo
AU - Eyers, David
AU - Bacon, Jean
AU - Papagiannis, Yiannis
AU - Shand, Brian
AU - Pietzuch, Peter
PY - 2010
Y1 - 2010
N2 - Continuous streams of event data are generated in many application domains including financial trading, fraud detection, website analytics and system monitoring. An open challenge in data management is how to analyse and react to large volumes of event data in real-time. As centralised event processing systems reach their computational limits, we need a new class of event processing systems that support deployments at the scale of thousands of machines in a cloud computing setting. In this poster we present SEEP, a novel architecture for event processing that can scale to a large number of machines and is elastic in order to adapt dynamically to workload changes.
AB - Continuous streams of event data are generated in many application domains including financial trading, fraud detection, website analytics and system monitoring. An open challenge in data management is how to analyse and react to large volumes of event data in real-time. As centralised event processing systems reach their computational limits, we need a new class of event processing systems that support deployments at the scale of thousands of machines in a cloud computing setting. In this poster we present SEEP, a novel architecture for event processing that can scale to a large number of machines and is elastic in order to adapt dynamically to workload changes.
UR - http://www.scopus.com/inward/record.url?scp=79958000173&partnerID=8YFLogxK
U2 - 10.1145/1930028.1930032
DO - 10.1145/1930028.1930032
M3 - Conference contribution
AN - SCOPUS:79958000173
SN - 9781450306010
T3 - Middleware'10 Posters and Demos Track, Middleware Posters'10
BT - Middleware'10 Posters and Demos Track, Middleware Posters'10
Y2 - 29 November 2010 through 3 December 2010
ER -