The general label-placement problem consists in labeling a set of features (points, lines, regions) given a set of candidates (rectangles, circles, ellipses, irregularly shaped labels) for each feature. The problem arises when annotating classical cartographical maps, diagrams, or graph drawings. The size of a labeling is the number of features that receive pairwise nonintersecting candidates. Finding an optimal solution, i.e. a labeling of maximum size, is NP-hard. We present an approach to attack the problem in its full generality. The key idea is to separate the geometric part from the combinatorial part of the problem. The latter is captured by the conflict graph of the candidates. We present a set of rules that simplify the conflict graph without reducing the size of an optimal solution. Combining the application of these rules with a simple heuristic yields near-optimal solutions. We study competing algorithms and do a thorough empirical comparison on point-labeling data. The new algorithm we suggest is fast, simple, and effective.

In a problem solving environment for geometric computing, a graphical user interface, or GUI, for visualization has become an essential component for geometric software development. In this paper we describe a visualization system, called GeoJAVA, which consists of a GUI and a geometric visualization library that enables the user or algorithm designer to (1) execute and visualize an existing algorithm in the library or (2) develop new code over the Internet. The library consists of geometric code written in C/C++. The GUI is written using the Java programming language. Taking advantage of the socket classes and system-independent application programming interfaces (API's) provided with the Java language, GeoJAVA oers a platform independent environment for distributed geometric computing that combines Java and C/C++. Users may remotely join a \channel" or discussion group in a location transparent manner to do collaborative research. The visualization of an algorithm, a C/C+...

. Frames will provide support for the programming of distributed memory machines via a library of basic algorithms, data structures and so-called programming frames (or frameworks). The latter are skeletons with problem dependent parameters to be provided by the users. Frames focuses on re-usability and portability as well as on small and easy-to-learn interfaces. Thus, expert and non-expert users will be provided with tools to program and exploit parallel machines eciently. Frames will be constructed for dierent target machines and common programming environments (like PVM or MPI). The focus, however, is on distributed-memory machines. Frames will be adapted optimally to the target systems, contain ecient state-of-the-art programming techniques, and therefore increase the usability and therefore the acceptance of parallel computing. Key words: Eciency, Re-usability, Portability, state-of-the-art algorithms, Templates, Skeletons, Frames 1 Motivation Parallel computing s...