Abstract
This work aims at improving the scalability of memory usage in Inductive Logic Programming systems. In this context, we propose two efficient data structures: the Trie, used to represent lists and clauses; and the RL-Tree, a novel data structure used to represent the clauses coverage. We evaluate their performance in the April system using well known datasets. Initial results show a substantial reduction in memory usage without incurring extra execution time overheads. Our proposal is applicable in any ILP system.

Abstract
This paper presents a new Web-based system, Mooshak, to handle programming contests. The system acts as a full contest manager as well as an automatic judge for programming contests. Mooshak innovates in a number of aspects: it has a scalable architecture that can be used from small single server contests to complex multi-site contests with simultaneous public online contests and redundancy; it has a robust data management system favoring simple procedures for storing, replicating, backing up data and failure recovery using persistent objects; it has automatic judging capabilities to assist human judges in the evaluation of programs; it has built-in safety measures to prevent users from interfering with the normal progress of contests. Mooshak is an open system implemented on the Linux operating system using the Apache HTTP server and the TcI scripting language. This paper starts by describing the main features of the system and its architecture with reference to the automated judging, data management based on the replication of persistent objects over a network. Finally, we describe our experience using this system for managing two official programming contests. Copyright (C) 2003 John Wiley Sons, Ltd.

Abstract
One of the major problems that actual logic programming systems have to address is whether and how to prune undesirable parts of the search space. A region of the search space would definitely be undesirable if it can only repeat previously found solutions, or if it is well-known that the whole computation will fail. Or it may be the case that we are interested in a subset of solutions. In this work we discuss how the BEAM addresses pruning issues. The BEAM is an implementation of David Warren's Extended Andorra Model. Because the BEAM relies on a very flexible execution mechanism, all cases of pruning discussed above should be considered. We show that all these different forms of pruning can be supported, and study their impact in applications.

Abstract
Pruning operators, such as cut, are important to develop efficient logic programs as they allow programmers to reduce the search space and thus discard unnecessary computations. For parallel systems, the presence of pruning operators introduces the problem of speculative computations. A computation is named speculative if it can be pruned during parallel evaluation, therefore resulting in wasted effort when compared to sequential execution. In this work we discuss the problems behind the management of speculative computations in or-parallel tabled logic programs. In parallel tabling, not only the answers found for the query goal may not be valid, but also answers found for tabled predicates may be invalidated. The problem here is even more serious because to achieve an efficient implementation it is required to have the set of valid tabled answers released as soon as possible. To deal with this, we propose a strategy to deliver tabled answers as soon as it is found that they are safe from being pruned, and present its implementation in the OPTYap parallel tabling system.

Abstract
ILP systems induce first-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from finding good models and increases the time to induce them. In this paper we propose a classification of hypotheses redundancy and show how expert knowledge can be provided to an ILP system to avoid it. Experimental results show that the number of hypotheses generated and execution time are reduced when expert knowledge is used to avoid redundancy.

Abstract
Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog by reusing answers to subgoals. The declarative nature of tabled logic programming suggests that it might be amenable to parallel execution. On the other hand, the complexity of the tabling mechanism, and the existence of a shared resource, the table, may suggest that parallelism might be limited and never scale for real applications. In this work, we propose three alternative locking schemes to deal with concurrent table accesses, and we study their impact on the OPTYap parallel tabling system using a set of tabled programs.