Abstract
While object-oriented programming languages are good at modelling real-world concepts and benefit from rich libraries and developer tools, logic programming languages are well suited for declaratively solving computational problems that require knowledge reasoning. Non-trivial declarative applications could take advantage of the modelling features of object-oriented programming and of the rich software ecosystems surrounding them. Linguistic symbiosis is a common approach to enable complementary use of languages of different paradigms. However, the problem of concepts leaking from one paradigm to another often hinders the applicability of such approaches. This issue has mainly been reported for object-oriented languages participating in a symbiotic relation with a logic language. To address this issue, we present LogicObjects, a linguistic symbiosis framework for transparently and (semi-) automatically enabling logic programming in Java, that aims to solve most of the problems of paradigm leaking reported in other works. © 2013 Springer-Verlag.

Abstract
We describe the portable and efficient implementation of coinductive logic programming found in Logtalk, discussing its features and limitations. As Logtalk uses as a back-end compiler a compatible Prolog system, we also discuss the status of key Prolog features for an efficient and usable implementation of coinduction. © 2013 Springer-Verlag.

Abstract
Map-Reduce is a programming model that has its roots in early functional programming. In addition to producing short and elegant code for problems involving lists or collections, this model has proven very useful for large-scale highly parallel data processing. In this work, we present the design and implementation of a high-level parallel construct that makes the Map-Reduce programming model available for Prolog programmers. To the best of our knowledge, there is no Map-Reduce framework native to Prolog, and so the aim of this work is to offer data processing features from which several applications can greatly benefit; the Inductive Logic Programming field, for instance, can take advantage of a Map-Reduce predicate when proving newly created rules against sets of examples. Our Map-Reduce model was comprehensively tested with different applications. Our experiments, using the Yap Prolog system, show that: (i) the model scales linearly up to 24 processors; (ii) a dynamic distributed scheduling strategy performs better than centralized or static scheduling strategies; and (iii) the performance varies significantly with the number of items being sent to each processor at a time. Overall, our Map-Reduce framework presents as a good alternative for both taking advantage of the currently available low cost multi-core architectures and developing scalable data processing applications, native to the Prolog programming language. © 2013 ACM.

Abstract
Logic Programming languages, such as Prolog, provide a high-level, declarative approach to programming. Despite the power, flexibility and good performance that Prolog systems have achieved, some deficiencies in Prolog's evaluation strategy - SLD resolution - limit the potential of the logic programming paradigm. Tabled evaluation is a recognized and powerful technique that overcomes SLD's susceptibility in dealing with recursion and redundant sub-computations. In a tabled evaluation, there are several points where we may have to choose between different tabling operations. The decision on which operation to perform is determined by the scheduling algorithm. The two most successful tabling scheduling algorithms are local scheduling and batched scheduling. In previous work, we have developed a framework, on top of the Yap Prolog system, that supports the combination of different linear tabling strategies for local scheduling. In this work, we propose the extension of our framework to support batched scheduling. In particular, we are interested in the two most successful linear tabling strategies, the DRA and DRE strategies. To the best of our knowledge, no other Prolog system supports both strategies simultaneously for batched scheduling. Our experimental results show that the combination of the DRA and DRE strategies can effectively reduce the execution time for batched evaluation.

Abstract

Abstract
Logic Programming languages, such as Prolog, provide an excellent framework for the parallel execution of logic programs. In particular, the inherent non-determinism in the way logic programs are structured makes Prolog very attractive for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Arguably, the most successful model for or-parallelism is environment copying, that has been efficiently used in the implementation of or-parallel Prolog systems both on shared memory and distributed memory architectures. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared with distributed memory architectures. Motivated by our past experience, in designing and developing parallel Prolog systems based on environment copying, we propose a novel computational model to efficiently exploit implicit parallelism from large scale real-world applications specialized for the novel architectures based on clusters of multicores. © João Santos and Ricardo Rocha.