||View materialization is commonly used to accelerate On-Line Analytical Processing (OLAP) operations. Views may be materialized at any of the three layers of a decision support system: (i) at the server side (i.e. the data warehouse), (ii) at a mid-tier, between the server and the client and, (iii) at the client side. Typically, static view selection is used at the server side, while dynamic approaches are employed for the other two cases. This thesis provides novel insights on view materialization techniques at all three layers: For the server-side layer, we propose the application of randomized local search algorithms, which provide near-optimal solutions in limited time for the view selection problem. In contrast to existing systematic techniques, randomized algorithms are applicable to high-dimensional data warehouses. Since a data warehouse is typically accessed simultaneously by many clients, we also investigated multiple-query optimization methods, which consider the available materialized views. Our methods exhibit considerably better scalability than the previously known ones. For the mid-tier, we propose a dynamic view materialization system, which is based on cooperative OLAP Cache Servers (OCS). An OCS is the equivalent of a proxy-server for web documents, but is designed to accommodate data from warehouses and support OLAP operations through the Internet. While this approach requires a dedicated infrastructure, we also investigate the alternative of enhancing common web proxy-servers with OLAP capabilities by means of active caching. Finally, at the client side, we propose a similar system, but the data are materialized locally at the clients instead of a mid-tier. By employing Peer-to-Peer technology, we publish the stored data of each client, in order to create a large virtual cache. Participation is ad-hoc, the system is fully distributed and supports adaptive reconfiguration. The experimental results indicate that our system amplifies the benefits of traditional client-side caching.