الفهرس 
CHAPTER ONE INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The Global economy is now dependent on the knowledge factor for growth. It is essential for government to pay more attention to knowledge precincts (KPs) as an important engine for economic growth and development. Egypt’s ICT (2012-2017) strategy adopted the goal of fostering knowledge based economy. In order to achieve this goal, a number of steps were to be implemented, one of them was increasing the number of KPs to reach 20 all over Egypt. KP location factor is not only one of the KPs critical success factors, but it can also be a contributing factor to sustainability and competitiveness. Changes of government decision making mechanisms are critical. No Decision Support System (DSS) was proposed to support decision makers in government to choose the best KP location that satisfies the decision maker’s preferences. Cloud computing is a distributed computing model that uses a network to share resources including data and applications like DSS. With the development of cloud computing, some cloud-based DSS were adopted for various sectors. Cloud computing delivering decision support as a service (DSaaS) and enabling scalability can offer significant benefits in diverse areas. Thus, developing an effective platform to assist government in the process of location decision making through a cloud environment is urgently required. This research proposes a decision support system offered in a cloud computing environment to the Egyptian Government to solve KP site selection problem. Location of KP’s considers factors that are relevant to the Egyptian economy such as; social, economic, technological infrastructure, human, and geographical factors. For these reasons, Multi Criteria Decision Making (MCDM) is used. MCDM approaches provide a systematic procedure to help decision makers to choose the most desirable and satisfactory alternative. Analytic Hierarchy Process (AHP) method is proposed in order to aid decision makers in government to choose the most appropriate KP location that satisfied the decision maker’s preferences. In this thesis scores for different location alternatives are calculated and the highest score is considered. In order to select the best KP location 5 main criteria and 33 sub-criteria were taken into consideration.