Abstract
Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog by reusing answers to subgoals. During tabled execution, several decisions have to be made. These are determined by the scheduling strategy. Whereas a strategy can achieve very good performance for certain applications, for others it might add overheads and even lead to unacceptable inefficiency. The ability of using multiple strategies within the same evaluation can be a means of achieving the best possible performance. In this work, we present how the YapTab system was designed to support dynamic mixed-strategy evaluation of the two most successful tabling scheduling strategies: batched scheduling and local scheduling.

Abstract

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
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 this work we present XmuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. xMuS er's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequence miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems.

Abstract
In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.