166 research outputs found

    Cloud Service Brokerage 2013 - Methods and Mechanisms

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    In the future, the Cloud will evolve into a rich ecosystem of service providers and consumers, each building upon the offerings of others. Cloud service brokers will play an important role, mediating between providers and consumers. As well as providing vertical integration and value-added aggregation of services, brokers will play an increased role in continuous quality assurance and optimization. This may range from setting common standards for service specification, providing mechanisms for lifecycle governance and service certification, to automatic arbitrage respecting consumer preferences, continuous optimization of service delivery, failure prevention and recovery at runtime. This workshop introduces some of these anticipated methods and investigates some of the mechanisms envisaged in future Cloud service brokerage

    Heuristic-based journey planner for mobility as a service (Maas)

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    © 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/su122310140The continuing growth of urbanisation poses a real threat to the operation of transportation services in large metropolitan areas around the world. As a response, several initiatives that promote public transport and active travelling have emerged in the last few years. Mobility as a Service (MaaS) is one such initiative with the main goal being the provision of a holistic urban mobility solution through a single interface, the MaaS operator. The successful implementation of MaaS requires the support of a technology platform for travellers to fully benefit from the offered transport services. A central component of such a platform is a journey planner with the ability to provide trip options that efficiently integrate the different modes included in a MaaS scheme. This paper presents a heuristic that implements a scenario-based journey planner for users of MaaS. The proposed heuristic provides routes composed of different modes including private cars, public transport, bike-sharing, car-sharing and ride-hailing. The methodological approach for the generation of journeys is explained and its implementation using a microservices architecture is presented. The implemented system was trialled in two European cities and the analysis of user satisfaction results reveal good overall performance.This research was funded by the European Union’s Horizon 2020 research and innovation programme grant number No 723176. And the APC was funded by the European Commission.Published versio

    MultiModal route planning in mobility as a service

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    This is an accepted manuscript of an article published by ACM in Proceedings 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WI 2019 Companion) in October 2019, available online: https://doi.org/10.1145/3358695.3361843 The accepted version of the publication may differ from the final published version.Mobility as a Service (MaaS) is a new approach for multimodal transportation in smart cities which refers to the seamless integration of various forms of transport services accessible through one single digital platform. In a MaaS environment there can be a multitude of multi modal options to reach a destination which are derived from combinations of available transport services. Terefore, route planning functionalities in the MaaS era need to be able to generate multi-modal routes using constraints related to a user's modal allowances, service provision and limited user preferences (e.g. mode exclusions) and suggest to the traveller the routes that are relevant for specific trips as well as aligned to her/his preferences. In this paper, we describe an architecture for a MaaS multi-modal route planner which integrates i) a dynamic journey planner that aggregates unimodal routes from existing route planners (e.g. Google directions or Here routing), enriches them with innovative mobility services typically found in MaaS schemes, and converts them to multimodal options, while considering aspects of transport network supply and ii) a route recommender that filters and ranks the available routes in an optimal manner, while trying to satisfy travellers' preferences as well as requirements set by the MaaS operator (e.g. environmental friendliness of the routes or promotion of specific modes of transport).Published versio

    A strategic management framework for leveraging knowledge assets

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    Implementing an IS strategy - A team approach

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    Modelling the regional economic impacts of energy development: a survey

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