We define Access-Controlled Temporal Networks (ACTNs) as an extension of Conditional Simple Temporal Networks with Uncertainty (CSTNUs). CSTNUs are able to handle features such as contingent durations and conditional constraints, and have thus been used to model the temporal constraints of workflows underlying business processes. However, CSTNUs are unable to model users and authorization constraints, and thus cannot model "who can do what, when". ACTNs solve this problem by adding users and authorization constraints that must be considered together with temporal constraints. Dynamic controllability (DC) of ACTNs ensures the existence of an execution strategy, able to assign tasks to authorized users dynamically, satisfying all the relevant authorization constraints no matter what contingent durations turn out to be or what conditional constraints have to be considered. We show that the DC checking can be done via Timed Game Automata and provide experimental results using UPPAAL-TIGA on a concrete real-world case study.

Access controlled temporal networks

Zavatteri, Matteo
2017

Abstract

We define Access-Controlled Temporal Networks (ACTNs) as an extension of Conditional Simple Temporal Networks with Uncertainty (CSTNUs). CSTNUs are able to handle features such as contingent durations and conditional constraints, and have thus been used to model the temporal constraints of workflows underlying business processes. However, CSTNUs are unable to model users and authorization constraints, and thus cannot model "who can do what, when". ACTNs solve this problem by adding users and authorization constraints that must be considered together with temporal constraints. Dynamic controllability (DC) of ACTNs ensures the existence of an execution strategy, able to assign tasks to authorized users dynamically, satisfying all the relevant authorization constraints no matter what contingent durations turn out to be or what conditional constraints have to be considered. We show that the DC checking can be done via Timed Game Automata and provide experimental results using UPPAAL-TIGA on a concrete real-world case study.
2017
ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence
978-989-758-219-6
978-989-758-220-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3441923
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