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

Storing data in relational databases has a long history since Codd defined the relational model and its normal forms in (Codd, 1970). Such relational databases still build the foundation for various applications throughout all application domains even with todays growing data volumes. It is assumed that, despite a rapid dissemination of In-Memory or NoSQL databases, relational databases will keep their important role. Hence, also relational databases are used as a foundation to store huge volumes of data and this is exactly where Cloud Computing offers dynamic and scalable capabilities. Renting such technological assets and capabilities from external cloud providers is an interesting approach. The pay-as-you-go character of these cloud offers, promise the usage of computing assets without large initial investments. In Cloud Computing environments, dedicated services are used for a certain time and are paid only for the respective usage. Moreover, as there are dedicated services, the complexity to integrate and use them is considered lower compared to paradigms like service-oriented architectures. As there are still open data security and data protection challenges, the usage of especially public Cloud Computing is far behind the expectations of e. g. Gartner (Carlton, 2013) and IDC (Gens & Shirer, 2013). Thus in this thesis, data security and data protection challenges for relational databases are addressed with the definition and an implementation of a framework for a SEcure and DI stributed C loud Data StOre, exploiting afixed vertical partitioning and distribution (FVPD) scheme. The main contribution of this work is to show that the proposed framework provides comparable response times to non-partitioned relational databases using cloud infrastructures and contemporary hardware devices.