Optimizing query strategies in fixed vertical partitioned and distributed databases and their application in semantic web databases

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Storing data in relational databases has a long history since Codd defined the relational model in 1970. Despite the rise of In-Memory and NoSQL databases, relational databases remain foundational across various applications, even as data volumes grow. They continue to serve as a basis for managing large datasets, particularly within the dynamic and scalable environment of Cloud Computing. Renting technological assets from external cloud providers offers a compelling approach, allowing users to take advantage of computing resources without significant upfront investments. The pay-as-you-go model enables users to access dedicated services for limited periods, paying only for what they use, which simplifies integration compared to service-oriented architectures. However, challenges related to data security and protection have hindered the adoption of public Cloud Computing, falling short of expectations from industry analysts. This thesis addresses these challenges by defining and implementing a framework for a Secure and Distributed Cloud Data Store, utilizing a fixed vertical partitioning and distribution scheme. The primary contribution of this work is demonstrating that the proposed framework achieves response times comparable to non-partitioned relational databases when deployed on cloud infrastructures with contemporary hardware.