Abstract
One of the advantages of logic programming in the fact that it offers many sources of implicit parallelism, such as and-parallelism and or-parallelism Recently, research has been concentrated on integrating the different forms of parallelism into a single combined system. In this work we concentrate on the problem of integrating or-parallelism and independent and-parallelism for parallel Prolog systems. We contend that previous data structures require pure recomputation and therefore do not allow for orthogonality between and parallelism and or-parallelism. In contrast, we submit that a simpler solution, the sparse binding array, does guarantee this goal, and explain in detail how independent and-parallelism and or-parallelism can thus be efficiently combined.

Abstract
We present an architecture that allows the seamless configuration of computer labs to work as dedicated computing clusters during periods of user inactivity. The operation of the cluster is fully automated by making use of differentiated network booting and a job management system. We have prepared it to be plugged to a larger computational grid. We provide some preliminary performance results obtained.

Abstract
Logic programming provides a high-level view of programming that gives implementor; a vast latitude in what techniques to research towards obtaining the best performance for logic programs. The Emended Andorra Model was designed towards achieving reduction of the search space whilst exploiting all the available parallelism in the application. The BEAM is a first sequential implementation for the Extended Andorra Model with Implicit Control, that has been shown to obtain good results. In this work we propose the RAINBOW, a parallel execution model for the BEAM. We present a general overview of how to distribute work, propose alternative approaches towards addressing the binding problem for the EAM, and present a scheduling strategy.

Abstract
In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Therefore, there is a great need for algorithms that are capable of building classifiers from very-large datasets and, simultaneously, being computationally efficient and scalable. One possible solution is to employ parallelism to reduce the amount of time spent in building classifiers from very-large datasets and keeping the classification accuracy. This work first overviews some strategies for implementing decision tree construction algorithms in parallel based on techniques such as task parallelism, data parallelism and hybrid parallelism. We then describe a new parallel implementation of the C4.5 decision tree construction algorithm. Even though the implementation of the algorithm is still in final development phase, we present some experimental results that can be used to predict the expected behavior of the algorithm. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
We propose a programming model for distributed concurrent systems with mobile objects in the context of a process calculus. Code mobility is induced by lexical scoping on names. Objects and messages migrate towards the site where their prefixes are lexically bound. Class definitions, on the other hand, are downloaded from the site where they are defined and are instantiated locally upon arrival. We provide several programming examples to demonstrate the expressiveness of the model. Finally, based on this model we describe an. architecture for a run-time system supporting concurrent, distributed computations and code mobility.

Abstract
We describe a programming language for distributed computations that supports mobile resources and is based on a process calculus. The syntax, semantics and implementation of the language are presented with a focus on the novel model of computation.