Virtual Private Ontology Server (VPOS) is a middleware software with focus on ontologies (semantic models). VPOS is offering a smart way to its users how to access relevant part of ontology dependent on their context. The user context can be expertise level or level of experience or job position in a hierarchy structure. Instead of having numerous numbers of ontologies associated to different user contexts, VPOS keeps only one ontology but offers sub-ontologies to users on the basis of their context. VPOS also supports reasoning to infer new consequences out of assertions stated in the ontology. These consequences are also visible for certain contexts which have access to enough assertions inside ontology to be able to deduct them. There are some issues within current implementation of VPOS. The application uses the random-access memory of local machine for loading the ontology which could be the cause of scalability issue when ontology size exceeds memory space. Also assuming that each user of VPOS holds her own instance of application it might result into maintainability issues such as inconsistency between ontologies of different users and waste of computational resources. This thesis project is about to find some practical solutions to solve the issues of current implementation, first by upgrading the architecture of application using new framework to address scalability issue and then moving to cloud addressing maintainability issues. The final production of this thesis project would be Cloud-VPOS which is an application made to deal with semantic web mechanisms and function over cloud plat-form. Cloud-VPOS would be an application where semantic web meets cloud computing by employing semantic web mechanisms as cloud services. / ebbits project (Enabling business-based Internet of Things and Services)

The main purpose of this master's thesis is to propose and implement a SAP CAR (customer activity repository) solution as an application into the current enterprise information system. Following the analysis of the existing system, there will be a solution created, which is fulfilling the customer demands and at the same time it is feasible under given conditions and in the current environment. This project is closely related with addressing the disaster recovery issue for making the system highly available. The final design is implemented, tested and then handed over to the customer.

Graph-structured data is ubiquitous and with the advent of social networking platforms has recently seen a significant increase in popularity amongst researchers. However, also many business applications deal with this kind of data and can therefore benefit greatly from graph processing functionality offered directly by the underlying database. This paper summarizes the current state of graph data processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research project SynopSys. With powerful graph pattern matching support at the core, we envision OLAP-like evaluation functionality exposed to the user in the form of easy-to-apply graph summarization templates. By combining them, the user is able to produce concise summaries of large graph-structured datasets. We also point out open questions and challenges that we plan to tackle in the future developments on our way towards large graph analytics.