Formal Specification and Validation of a Data-driven Software System for Fire Risk Prediction
Peer reviewed, Journal article
Published version
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https://hdl.handle.net/11250/3060729Utgivelsesdato
2022Metadata
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Originalversjon
CEUR Workshop Proceedings. 2022, 3170 1-20.Sammendrag
Long periods of dry and cold weather conditions significantly increase fire risks for wooden buildings. Recent advances in predictive fire risk models combined with publicly available cloud-based weather data services have enabled the development of smart software systems for location-oriented fire risk notification. We have developed a Coloured Petri Net (CPN) model specifying the software architecture of a microservice-based predictive fire risk notification system. The CPN model captures the set of micro-services provided via REST APIs and the interaction between the constituent services for location tracking and subscription, fire risk computation, and data harvesting. As part of the work, we present a general modelling approach and pattern for REST-based APIs. We apply simulation and state space exploration to validate and verify key behavioural properties of the predictive fire risk notification system.