Special issue on Remote visualization

Abstract

The visualization of huge datasets may be required by a broad spectrum of scientific applications encompassing, among others, the medicine, chemistry, architecture and aero-space fields. Unfortunately, in some cases, visualization devices might be unable to locally store such data. Datasets could be either too large (thus requiring to be stored in dedicated data centers) or might need specialized hardware to be processed (like high performance computers or cloud computing architectures). Some applications could even prevent, e.g., for privacy and/or security issues, a data transfer from data centers to (client) visualization devices. Under these circumstances, only representations of data, rather than data themselves, could be sent to the client side. Remote visualization has been often regarded as an interesting solution to the above problems, as data can be remotely processed where resources are available and only their representations are to be managed by the visualization devices. Although with the remote visualization paradigm it can be possible to minimize the resources required on the client side, it might also involve several issues basically related to the underlying network channel. In fact, network bandwidth, latency and interactive frame rates are the main parameters to be taken into account when data processing and data visualization are two logically and physically separated processes. A lot of work is known in the literature mainly related to the reduction of the network load from data centers to client devices. Compression scheme have been used to reduce the amount of data to be transmitted, both considering flows of