The delivery of services to citizens by Public Administrations requires to set up and coordinate complex Business Processes. Typically homogeneous Public Administrations, such as municipalities, have to provide the same services to all citizens. Nevertheless their concrete implementation, and the supporting Business Process model and data object models, can slightly differ from one Public Administration to the other due to organizational factors. If such variability is not explicitly represented and managed, each office will have to reflect on and analyse the requirements posed by the delivery of the service; then they will have to derive a specific process and data model. On the other hand the explicit modeling of variability can reduce the work to be done and permits to define general specifications from which specific model variants can be derived according to specific needs. In this paper we propose a novel approach, inspired by Feature Modeling techniques, for data object variability modeling that can be used to provide high level blueprints from which detailed Business Processes and data object specifications can be derived. Finally, a complex scenario has been applied to validate the approach with encouraging results.

A Data Oriented Approach to Derive Public Administration Business Processes

COGNINI, RICCARDO;CORRADINI, Flavio;FORNARI, FABRIZIO;POLINI, Andrea;RE, Barbara
2015-01-01

Abstract

The delivery of services to citizens by Public Administrations requires to set up and coordinate complex Business Processes. Typically homogeneous Public Administrations, such as municipalities, have to provide the same services to all citizens. Nevertheless their concrete implementation, and the supporting Business Process model and data object models, can slightly differ from one Public Administration to the other due to organizational factors. If such variability is not explicitly represented and managed, each office will have to reflect on and analyse the requirements posed by the delivery of the service; then they will have to derive a specific process and data model. On the other hand the explicit modeling of variability can reduce the work to be done and permits to define general specifications from which specific model variants can be derived according to specific needs. In this paper we propose a novel approach, inspired by Feature Modeling techniques, for data object variability modeling that can be used to provide high level blueprints from which detailed Business Processes and data object specifications can be derived. Finally, a complex scenario has been applied to validate the approach with encouraging results.
2015
978-1-61499-570-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11581/388263
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