78,821 research outputs found
ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime
Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems.
Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements.
Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements.
Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor.
Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in todayâs complex system environments.Peer ReviewedPostprint (author's final draft
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Public policy that does the right thing rather than the wrong thing righter
Motivated by the reprisal of âwicked problemsâ in Australian public policy discourse we make the case for understanding climate change adaptation, water and river managing, and other complex, uncertain, natural resource issues as âwicked problemsâ. This âframingâ of social planning dilemmas dates back 40 years yet public policy practitioners still do not seem well equipped in terms of understandings and practices to engage with these situations and to effect systemic improvements. Drawing on a decade of research in Europe we make the case for investing in social learning as an alternative governance mechanism and as a form of praxis for managing in âwicked problemâ situations. We outline our main research findings to explain how we understand and enact social learning. In doing so, we also draw on the Open University UKâs 35 years of experience of teaching systems thinking and practice for managing âwicked problemsâ. We conclude by opening up an invitational space to explore the commonalities and differences in research on social learning with that on deliberative practices and governance
Event-driven Adaptation in COP
Context-Oriented Programming languages provide us with primitive constructs
to adapt program behaviour depending on the evolution of their operational
environment, namely the context. In previous work we proposed ML_CoDa, a
context-oriented language with two-components: a declarative constituent for
programming the context and a functional one for computing. This paper
describes an extension of ML_CoDa to deal with adaptation to unpredictable
context changes notified by asynchronous events.Comment: In Proceedings PLACES 2016, arXiv:1606.0540
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River basin planning project: social learning (Science Report SC050037/SR1)
This report documents the findings of a 12-month Environment Agency science project on social learning for river basin planning. Our aim was to use social learning approaches and soft system methods to inform the development of the River Basin Planning Strategy and improve the effectiveness of the Environment Agency's Water Framework Directive (WFD) Programm
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