JIHAD EL SANA

In this paper we are presenting a novel approach that enables the rendering of large-shared datasets at interactive rates using inexpensive workstations. Our algorithm is based on view-dependent rendering and client-server technology-servers host large datasets and manage the selection of the various levels of detail, while clients receive blocks of update operations which are used to generate the appropriate level of detail in an incremental manner. We assume that servers are capable machines in terms of storage capacity and computational power and clients are inexpensive workstations which have limited 3D rendering capabilities. For optimization purposes we have developed two similar approaches-one for local area networks and the other for wide area networks. For the second approach we have performed several changes to adapt to the limitation of the wide area networks. To avoid network latency we have developed two powerful mechanisms that cache the adapt operation blocks on the clients' side and predict the future view-parameters of clients based on their recent behavior. Our approach dramatically reduces the amount of memory used by each client and the entire computing system since the dataset is stored only once in the local memory of the server. In addition, it decreases the load on the network as a result of the incremental update contributed by view-dependent rendering.