A parallel optimisation technique for large join queries is presented. The technique processes the search space of query execution plans twice: the first scan is based on iterative improvement; the second scan uses the results of the first one to reduce the search space, in order to find an optimal solution faster. Two scheduling algorithms are studied for the imple-mentation of the technique. Experiments showing the behaviour of the technique are presented.

This article investigates the effectiveness of voting and stacked generalization -also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the meta-level. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems.

In this study we present a technique for the parallel optimisation of join queries, that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards iterative improvement. This technique has been developed on a low-parallelism transputer-based architecture, where its behaviour is studied for the optimisation of queries with many tenths of joins.

This paper presents a new framework for extracting information from collections of Web pages across different sites. In the proposed framework, a standard wrapper induction algorithm is used that exploits named entity information that has been previously identified. The idea of post-processing the extraction results is introduced for resolving ambiguous fields and improving the overall extraction performance. Post-processing involves the exploitation of two additional sources of information: field transition probabilities, based on a trained bigram model, and confidence scores, estimated for each field by the wrapper induction system. A multiplicative model that is based on the product of those two probabilities is also considered for post-processing. Experiments were conducted on pages describing laptop products, collected from many different sites and in four different languages. The results highlight the effectiveness of the new framework.

In this study we present a technique for the parallel optimisation of join queries that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards iterative improvement. This technique has been developed on a low-parallelism transputer-based architecture, where its behaviour is studied for the optimisation of queries with many tens of joins.