Computer Studies

As computational power and storage capacity increase, processing and analyzing large vol-umes of multi-dimensional datasets play an increasingly important part in many domains of sci-enti c research. Several database research groups and vendors have developed object-relational database systems to provide some support for managing and/or visualizing multi-dimensional datasets. These systems, however, provide little or no support for analyzing or processing these datasets { the assumption is that this is too application-specic to warrant common support. As a result, applications that process these datasets are usually decoupled from data storage and management, resulting in ineciency due to copying and loss of locality. Furthermore, every application developer has to implement complex support for managing and scheduling the processing. Our study of a large set of scientic applications over the past three years indicates that the processing for such datasets is often highly stylized and shares several important charac-teristics. Usually, both the input dataset as well as the result being computed have underlying multi-dimensional grids. The basic processing step usually consists of transforming individua