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.

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
This Doctoral Consortium paper focuses on Extreme Adaptivity, a set of top level requirements for adaptive hypertext systems, which has resulted from one year of examining the adaptive hypertext landscape. The complete specification of a system, KnowledgeAtoms, is also given, mainly as an example of Extreme Adaptivity. Additional methodological elements are discussed.

Abstract
We describe the MyYapDB, a deductive database system coupling the Yap Prolog compiler and the MySQL DBMS. We use our OPTYap extension of the Yap compiler, which is the first available system that can exploit parallelism from tabled logic programs. We describe the major features of the system, give a simplified description of the implementation and present a performance comparison of using static facts or accessing the facts as MySQL tuples for a simple example.