Abstract
Parallel programming has become mandatory to fully exploit the potential of modern CPUs. The data-flow model provides a natural way to exploit parallelism. However, traditional data-flow programming is not trivial: specifying dependencies and control using fine-grained tasks (such as instructions) can be complex and present unwanted overheads. To address this issue we have built a coarse-grained data-flow model with speculative execution support to be used on top of widespread architectures, implemented as a hybrid Von Neumanm/data-flow execution system. We argue that speculative execution fits naturally with the data-flow model. Using speculative execution liberates the programmer to consider only the main dependencies, and still allows correct data-flow execution of coarse-grained tasks. More- over, our speculation mechanism does not demand centralised control, which is a key feature for upcoming many-core systems, where scalability has become an important concern. An initial study on a artificial bank server application suggests that there is a wide range of scenarios where speculation can be very effective. © 2010 IEEE,.

Abstract
Grid environments are ideal for executing applications that require a huge amount of computational work, both due to the big number of tasks to execute and to the large amount of data to be analysed. Unfortunately, current tools may require that users deal themselves with corrupted outputs or early termination of tasks. This becomes incovenient as the number of parallel runs grows to easily exceed the thousands. ReCS is a user-level software designed to provide automatic detection and restart of corrupted or early terminated tasks. ReGS uses a web interface to allow the setup and control of grid execution, and provides automatic input data setup. ReGS allows the automatic detection of job dependencies, through the GRID-ADL task management language. Our results show that besides automatically and effectively managing a huge number of tasks in grid environments, ReGS is also a good monitoring tool to spot grid nodes pitfalls. © 2005 IEEE.

Abstract
One of the advantages of logic programming is the fact that it offers several sources of implicit parallelism. One particularly interesting form of And-Parallelism is Independent And-Parallelism (IAP). Most work on the implementation of IAP is based on Hermenegildo's RAP-WAM. Unfortunately there are some drawbacks associated with the classical approaches based on the use of parcalls and markers. One first observation is that the introduction of parcall frames significantly slows down sequential execution. Moreover, it may result in fine-grained parallel work. We found these problems to be particularly significant in the context of the implementation of combined AND/OR systems. In this paper we take a fresh look at this issue. Our goal is to start from a standard sequential Prolog implementation and try to discover the minimal number of changes that would be required for an efficient implementation of IAP. The key ideas in our design are to (i) to always take advantage of analogy between or-parallelism and IAP; (ii) to avoid creating new structures by adapting preexistingx WAM data-structures wherever possible; and (iii) to avoid major changes to the compiler. The authors would like to acknowledge and thank the contribution and support from Fernando Silva. The work has also benefitted from discussions with Luis Fernando Castro, Ines de Castro Dutra, Kish Shen, Gopal Gupta, and Enrico Pontelli. Our work has been partly supported by Fundaçã da Ciencia e Tecnologia and JNICT under the projects Melodia (JNICT/PBIC/C/TIT/2495/95) and Dolphin (PRAXIS/2/2.l/TIT/1577/95). © 2000 Published by Elsevier B.V.

Abstract
We argue that in order to exploit both Independent And-and Or-parallelism in Prolog programs there is advantage in recomputing some of the independent goals, as opposed to all their solutions being reused. We present an abstract model, called the Composition-tree, for representing and-or parallelism in Prolog programs. The Composition-tree closely mirrors sequential Prolog execution by recomputing some independent goals rather than fully re-using them. We also outline two environment representation techniques for And-Or parallel execution of full Prolog based on the Composition-tree model abstraction. We argue that these techniques have advantages over earlier proposals for exploiting and-or parallelism in Prolog. © 1993 Ohmsha, Ltd. and Springer.

Abstract
A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women. © 2012 Springer-Verlag.

Abstract
"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k?-?medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters. © 2012 Springer-Verlag Berlin Heidelberg.