Business Model Approach to Public Service Innovation

The operating environment of the public sector has undergone a fundamental shift towards a more competitive nature. As these changes accelerate, they are exerting considerable pressure on the government in terms of rising costs and ever-increasing need for innovative service offerings. In order to shed light on these contemporary challenges, this chapter will review and analyse a number of innovative service delivery modes observed in practice, including joint ventures with the private and not-for-profit sectors, public private partnerships, contracting out, franchising, and the use of social bonds and collaborative services. By presenting a new ‘business model’ designed specifically for decision makers in the public sector, this chapter will equip the readers with the means to better understand and manage public service innovations in the increasingly challenging environment

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 h ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. In this work, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to (Formula presented.). The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 h ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. In this work, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to (Formula presented.). The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance

Towards a foundation for intelligent contracts

Computer Systems, Imagery and Medi

Supporting end-to-end resource virtualization for Web 2.0 applications using Service Oriented Architecture

In recent years, technologies have been introduced offering a large amount of computing and networking resources. New applications such as Google AdSense and BitTorrent can profit from the use of these resources. An efficient way of discovering and reserving these resources is by using the Service Oriented Architecture (SOA) concept. SOA can be considered as a philosophy or paradigm in organizing and utilizing services and capabilities that may be under the control of different ownership domains. This paper presents an architecture that can be used to support end-to-end resource virtualization for Web 2.0 applications and in particular for peer-to-peer applications by using the Service Oriented Architecture concept