Abstract
A critical component in the implementation of an efficient tabling system is the design of the data structures and algorithms to access and manipulate tabled data. Arguably, the most successful data structure for tabling is tries. However, when used in applications that pose many queries and/or have a large number of answers, tabling call build arbitrarily many and/or very large tables, quickly filling Lip memory. In this paper, we propose a new design for the table space organization where all terms in tabled subgoal calls and tabled answers are represented only once in a common global trie instead of being spread over several different trie data structures. Our initial experiments using the YapTab tabling system show significant reductions oil memory usage without compromising running time.

Abstract
Resolution strategies based on tabling are considered to be particularly effective in Logic Programming. Unfortunately, when faced with applications that compute large and/or many answers, memory exhaustion is a potential problem. In such cases, table deletion is the most common approach to recover space. In this work, we propose a different approach, storing tables into a relational database. Subsequent calls to stored tables import answers from the database, rather than performing a complete re-computation. To validate this approach, we have extended the YapTab tabling system, providing engine support for exporting and importing tables to and from the MySQL RDBMS. Three different relational models for data storage and two recordset retrieval strategies are compared.

Abstract
Tabling is all implementation technique that overcomes some limitations of traditional Prolog systems in dealing with redundant sub-computations and recursion. The performance of tabled evaluation largely depends on the implementation of the table space. Arguably, the most successful data structure for tabling is tries. However, while tries are efficient for variant based tabled evaluation, they are limited ill their ability to recognize and represent repeated answers for different calls. In this paper, we propose a new design for the table space where tabled subgoal calls and/or answers are stored only once in a common global trie instead of being spread over several different tries. Our preliminary experiments using the YapTab tabling system show very promising reductions on memory usage.

Abstract
Prolog is the most; well-known and widely used logic programming language. A large number of Prolog applications maintains information by asserting and retracting clauses from the database. Such dynamic predicates raise a number of issues for Prolog implementations, such as what are the semantics of a procedure where clauses can be retracted and asserted while the procedure is being executed. One advantage of Logical Update semantics is that it allows indexing. In this paper, we discuss how one call implement just-in-time indexing with Logical Update semantics. Our algorithm is based on two ideas: stable structure and fragmented index trees. By stable structure one means that we define a structure for the indexing tree that not change, even as we assert and as we retract clauses. Second, by fragmented index tree we mean that the indexing tree will be built in such a way that the updates will be local to each fragment. The algorithm was implemented and results indicate significant speedups and reduction of memory usage in test applications.

Abstract
The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.

Abstract
Logic programming provides an ideal framework for tackling complex data, such as the multi-dimensional vector-based data used to represent spatial databases. Unfortunately, the usefulness of logic programming systems if often hampered by the fact that most of these systems have to rely on a single unification-based mechanism as the only way to search in the database. While unification can usually take effective advantage of hash-based indexing, it is often the case that queries over more complex and structured data, such as the vectorial terms stored in spatial databases, cannot. We propose a new extension to Prolog indexing: User Defined Indexing (UDI). In this mechanism, the programmer may add extra information to Prolog indices so that only interesting fragments of the database will be selected. UDI provides a general extension of indexing, and can be used for both instantiated and constrained variables. As a test case, we demonstrate how UDI can be combined with a constraint system to provide an elegant and efficient mechanism to generate and execute range queries and spatial queries. Experimental evaluation shows that this mechanism can achieve orders of magnitude speedups on non-trivial datasets.