A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers

Development of Disaster Resilient Coastal Communities to Enhance Economic Development and Social Welfare: Book of Abstracts

Coast at risk – the importance of risk knowledge Coastal communities all over the world are under severe pressure resulting from planned and unplanned development, population growth and human induced vulnerability, coastal hazards with increasing frequency and magnitude and impacts of global climate change. These unprecedented changes have increased the level of risk of such coastal communities from a wide range of coastal hazards arising from natural phenomena and human induced activities. In this respect the assessment and management of risk for coastal hazards plays a vital role for safety of human lives, conservation of ecosystems and protection of the built environment. It leads to the development of disaster resilient communities to enhance economic development and social welfare. Risk assessment is one of the fundamental first steps towards planning, improving and implementing effective disaster risk reduction policies and programmes. One has to know and identify risks if they are to be effectively reduced and contained. There is a need to develop simplified approaches to risk assessment to convince a wider stakeholder base that investing in risk assessments pay. Such approaches bring together so many members of civil society leading the efforts to make disaster risk reduction everyone’s business

What works in changing energy-using behaviours in the home? A rapid evidence assessment

RAND Europe was commissioned by the Department of Energy & Climate Change (DECC) to undertake a Rapid Evidence Assessment* to understand “What works in changing energy-using behaviours in the home?”. The main objective was to answer this question by systematically reviewing the evidence around domestic behaviour change, with a particular focus on international evidence.In order to identify relevant studies, and avoid overlap with other previous evidence reviews, a set of search criteria was established. For inclusion, studies must:• Target energy-using behaviours in the home.• Consider at least one intervention.**• Go beyond the use of direct feedback on past energy use and pricing strategies to shift or reduce demand; and consider behaviour beyond one-off purchasing decisions (such as the installation of insulation or the purchase of energy-efficient appliances).• Measure a behaviour change in a real-world setting, either observed or self-reported.• Make a comparison between groups (e.g. between treatment and control groups), or across different time periods.No restrictions were applied regarding sample size; and both quantitative and qualitative studies were included.This report draws on 48 behaviour change programmes identified and selected through a systemic search process. These programmes involve a wide range of innovative approaches (such as the provision of Home Energy Reports that compare households’ consumption with their neighbours’) as well as more traditional approaches (including advertising campaigns)

Proceedings of ARCOM Doctoral Workshop on 'Industry 4.0 and Disaster Resilience in the Built Environment': ARCOM Doctoral Workshop in association with CIB W120 - Disasters and the Built Environment

Disruptive innovations of the 4th industrial revolution are now starting to make an impact on construction. Although construction has lagged behind some of the other industries in embracing this revolution, recent years have seen a concentrated effort to drive change in construction processes and practices. The 4th industrial revolution is characterised by technologies such as digitisation, optimisation, and customisation of production, automation and adaptation; as well as processes such as human machine interaction; value-added services and businesses, and automatic data exchange and communication. In construction, the applications of Industry 4.0 include 3D printing of building components, autonomous construction vehicles, the use of drones for site and building surveying, advanced offsite manufacturing facilities etc. The application of technologies, processes associated with Industry 4.0 is seen to be already making an impact on construction, and reshaping the future of built environment. This new digital era of construction, fuelled by Industry 4.0, has significant potential to enhance disaster resilience practices in the built environment. Knowledge on resilience of the built environment including preparedness, response and recovery has advanced significantly over the recent years and we are now in an era where resilience is seen as a key constituent of the built environment. But the recurring and devastating impacts of disasters constantly challenge us to improve our practices and seek ways of achieving greater heights in our quest of achieving a resilient built environment. It is often proposed that the innovations associated with Industry 4.0 joined by IoTs and sensors can be exploited to enhance the ability of the built environment to prepare for and adapt to climate change and withstand and recover rapidly from the impacts of disasters. This integration of cyber physical systems through IoTs needs a holistic view of disaster resilience. Often, the focus is on benefits individual technologies can offer. However, the ability to integrate different aspects of disaster resilience using a range of new technologies promise to deliver wider benefits beyond and above what individual technologies can offer. For instance, an integrated digital twin allows to bring together advanced risk modelling, big data, cloud computing, internet of things, advanced off-site manufacturing, etc. together to deliver a resilient built environment. This requires careful planning and extensive research on the complexities surrounding disaster resilience related aspects and the use of related data. The ultimate objective of any new innovation, including Industry 4.0, should ideally be to benefit the society. The society that we live today is often disrupted by natural hazard induced disasters, whether it be floods, cyclones, earthquakes, landslides or tsunamis. The challenge that is in front of us is to effectively utilise new innovations driven by digital information to enhance disaster resilience in our buildings, communities, cities and regions. However, unlike earlier industrial revolutions, digital revolution is not easy to control. We must ensure that the fundamental values such as freedom, openness and pluralism are inbuilt in these new technologies. This is an uncharted territory for us. In addition to addressing complexities and challenges of using Industry 4.0 technologies, we also need to have policies and guidelines on the use of information. There should be a balance between innovation and regulation. We are confident that by bringing together researchers, practitioners and policy-makers alike from relevant disciplines we can deliver realistic benefits to transform our disaster resilience practices and policies, and make the built environment we live in more resilient

Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime