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Opportunistic techniques have been widely used to create useful computational infrastructures and have demonstrated an ability to deliver computing resources to large applications. However, the management of disk space and usage in such systems is often neglected. While overarching filesystemsstructures and policies for their data.

Pervasive computing technologies, together with the increasing participation of the Web community in feeding geo-located information within tools such as Google Earth, will soon make available a huge amount of real-time information about the physical world and its processes. This opens up the possibility of exploiting all such information for the ubiquitous provisioning of context-aware services for “browsing the world” around us. However, for this to occur, proper general-purpose data models and software infrastructures must be developed. In this chapter, we propose a simple, yet effective model for the representation of heterogeneous contextual information and the design and implementation of a general user-centric infrastructure for ubiquitous browsing of the world. The presentations of some exemplar services we have implemented over it and have made available to users via Google Earth interfacing complete the chapter.

Computers are most frequently used for processing text, or for processing signalbased data such as graphics, audio and video. However, there is also a significant amount of non-textual symbol-based data, such as musical scores, mathematics, schematics, and scientific models, that are encoded in such a way that they can be manipulated by algorithms at a higher level than simply processing the signal. Such data can be used for complex search queries, for example.

This chapter describes those machines which do not fit easily into any of our other chapters. One manufacturer, Teradata, has concentrated its efforts on producing a super-database machine, while the others have created niches for themselves in the workstation market. Together, they show that parallelism offers more than just a faster solution to the same old problems.

Computers by themselves are nothing more than expensive room heaters; what makes them valuable is the software that runs on them. Unfortunately, robust, machine-independent software environments for parallel computers have been notable to date primarily by their absence. While many groups are working toward compilers which will automatically parallelise programs, others have concentrated their efforts on producing systems which will let users write programs which can run on a wide range of parallel platforms. In this chapter we discuss four such systems and try to evaluate their strengths and weaknesses.

We present a new formalism called GAMMA which relies on the chemical reaction metaphor; the only data structure is the multiset and the computation can be seen as a succession of chemical reactions consuming elements of the multiset and producing new elements according to specific rules. We show the relevance of this formalism with respect to program development by proposing a systematic program derivation method.