QoS-oriented service management in clouds for large scale industrial activity recognition

Voulodimos, AS; Kyriazis, DP; Gogouvitis, SV; Doulamis, AD; Kosmopoulos, DI; Varvarigou, TA

dc.contributor.author	Voulodimos, AS	en
dc.contributor.author	Kyriazis, DP	en
dc.contributor.author	Gogouvitis, SV	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Kosmopoulos, DI	en
dc.contributor.author	Varvarigou, TA	en
dc.date.accessioned	2014-03-01T02:53:26Z
dc.date.available	2014-03-01T02:53:26Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36319
dc.subject	activity recognition	en
dc.subject	cloud infrastructure	en
dc.subject	industrial workflows	en
dc.subject	QoS	en
dc.subject	service management	en
dc.subject.other	Activity recognition	en
dc.subject.other	Decentralized approach	en
dc.subject.other	Industrial activities	en
dc.subject.other	Industrial enterprise	en
dc.subject.other	Industrial environments	en
dc.subject.other	Interactivity	en
dc.subject.other	Machine-learning	en
dc.subject.other	Multiple cameras	en
dc.subject.other	Real time performance	en
dc.subject.other	Recognition rates	en
dc.subject.other	Resource limitations	en
dc.subject.other	Safety guarantees	en
dc.subject.other	Service management	en
dc.subject.other	Service-based	en
dc.subject.other	Time series classifications	en
dc.subject.other	Training sets	en
dc.subject.other	Work-flows	en
dc.subject.other	Accident prevention	en
dc.subject.other	Computer vision	en
dc.subject.other	Industry	en
dc.subject.other	Quality of service	en
dc.subject.other	Soft computing	en
dc.subject.other	Time series	en
dc.subject.other	Cloud computing	en
dc.title	QoS-oriented service management in clouds for large scale industrial activity recognition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/SoCPaR.2011.6089156	en
heal.identifier.secondary	http://dx.doi.org/10.1109/SoCPaR.2011.6089156	en
heal.identifier.secondary	6089156	en
heal.publicationDate	2011	en
heal.abstract	Motivated by the need of industrial enterprises for supervision services for quality, security and safety guarantee, we have developed an Activity Recognition Framework based on computer vision and machine learning tools, attaining good recognition rates. However, the deployment of multiple cameras to exploit redundancies, the large training set requirements of our time series classification models, as well as general resource limitations together with the emphasis on real-time performance, pose significant challenges and lead us to consider a decentralized approach. We thus adapt our application to a new and innovative real-time enabled framework for service-based infrastructures, which has developed QoS-oriented Service Management mechanisms in order to allow cloud environments to facilitate real-time and interactivity. Deploying the Activity Recognition Framework in a cloud infrastructure can therefore enable it for large scale industrial environments. © 2011 IEEE.	en
heal.journalName	Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011	en
dc.identifier.doi	10.1109/SoCPaR.2011.6089156	en
dc.identifier.spage	556	en
dc.identifier.epage	560	en