After retiring from a career in academic/IT management at Carnegie-Mellon, Northeastern, and Washington and Lee Universities, John Stuckey is serving as Acting Chief Technology Officer at the American University in Cairo. This is the third of his reports to Ubiquity from Egypt.

The term CBIR seems to have originated in 1992, when it was describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and VLSI design [1].

Computational grids are very large-scale aggregates of communication and computation resources enabling new types of applications and bringing several benefits of economy-of-scale. The first computational grids were established in academic environments during the previous decade, and today are making inroads into the realm of corporate and enterprise computing.

Very recently, we observe the emergence of cloud computing as a new potential super structure for corporate, enterprise and academic computing. While cloud computing shares the same original vision of grid computing articulated in the 1990s by Foster, Kesselman and others, there are significant differences.

In this paper, we first briefly outline the architecture, technologies and standards of computational grids. We then point at some of notable examples of academic use of grids and sketch the future of research in grids. In the third section, we draw some architectural lines of cloud computing, hint at the design and technology choices and indicate some future challenges. In conclusion, we claim that academic computing clouds might appear soon, supporting the emergence of Science 2.0 activities, some of which we list shortly.