Today's business applications are increasingly process driven, meaning that the main application logic is executed by a dedicate process engine. In addition, component-oriented software development has been attracting attention for building complex distributed applications. In this paper we present the experiences gained from building a process-driven biometric identification application that makes use of Grid infrastructures via the Grid Component Model (GCM). GCM, besides guaranteeing access to Grid resources, supports autonomic management of notable parallel composite components. This feature is exploited within our biometric identification application to ensure real time identification of fingerprints. Therefore, we briefly introduce the GCM framework and the process engine used, and we describe the implementation of the application by means of autonomic GCM components. Finally, we summarize the results, experiences, and lessons learned focusing on the integration of autonomic GCM components and the process-driven approach.

Process-Driven Biometric Identification by means of Autonomic Grid Components

ALDINUCCI, MARCO;
2012-01-01

Abstract

Today's business applications are increasingly process driven, meaning that the main application logic is executed by a dedicate process engine. In addition, component-oriented software development has been attracting attention for building complex distributed applications. In this paper we present the experiences gained from building a process-driven biometric identification application that makes use of Grid infrastructures via the Grid Component Model (GCM). GCM, besides guaranteeing access to Grid resources, supports autonomic management of notable parallel composite components. This feature is exploited within our biometric identification application to ensure real time identification of fingerprints. Therefore, we briefly introduce the GCM framework and the process engine used, and we describe the implementation of the application by means of autonomic GCM components. Finally, we summarize the results, experiences, and lessons learned focusing on the integration of autonomic GCM components and the process-driven approach.
2012
5(3)
274
291
http://www.inderscience.com/info/inarticletoc.php?jcode=ijaacs&year=2012&vol=5&issue=3
autonomic computing; grid component model; biometric identification; parallel applications; distributed applications; process-driven applications
Thomas Weigold; Marco Aldinucci; Marco Danelutto; Vladimir Getov
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/113482
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