Abstract
The amount of data collected and stored in databases is growing considerably in almost all areas of human activity. In complex applications the data involves several relations and proposionalization is not a suitable approach. Multi-Relational Data Mining algorithms can analyze data from multiple relations, with no need to transform the data into a single table, but are computationally more expensive. In this paper a novel relational classification algorithm based on the k-nearest neighbour algorithm is presented and evaluated.

Abstract
Despite the considerable success of Inductive Logic Programming (ILP), deployed ILP systems still have efficiency problems when applied to complex problems. Several techniques have been proposed to address the efficiency issue. Such proposals include query transformations, query packs, lazy evaluation and parallel execution of ILP systems, to mention just a few. We propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique takes advantage of the two stage procedure of Mode Directed Inverse Entailment (MDIE) systems. In the first stage of a MDIE system, where the bottom clause is constructed, we store not only the bottom clause but also valuable additional information. The information stored is sufficient to evaluate the clauses constructed in the second stage without the need for a theorem prover. We used a data structure called Trie to efficiently store all bottom clauses produced using all examples (positive and negative) as seeds. The technique was implemented and evaluated using two well known data sets from the ILP literature. The results are promising both in terms of execution time and accuracy.

Abstract
Inductive Logic Programming (ILP) is a powerful and well-developed abstraction for multi-relational data mining techniques. Despite the considerable success of ILP, deployed ILP systems still have efficiency problems when applied to complex problems. In this paper we propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique is based on the Mode Directed Inverse Entailment approach to ILP, where a bottom clause is generated for each example and the generated clauses are subsets of the literals of such bottom clause. We propose to store in a prefix-tree all clauses that can be generated from all bottom clauses together with some extra information. We show that this information is sufficient to estimate the number of examples that can be deduced from a clause and present an ILP algorithm that exploits this representation. We also present an extension of the algorithm where each prefix-tree is computed only once (compiled) per example. The evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis' value in theory-level search, in cross-validation evaluation procedures and in parameter tuning. Both proposals are empirically evaluated on real applications and considerable speedups were observed.

Abstract
We report on the development of a Decision Support System (DSS) to plan the best assignment for the weekly promotion space of a TV station. Each product to promote has a given target audience that is best reached at specific time periods during the week. The DSS aims to maximize the total viewing for each product within its target audience while fulfilling a set of constraints defined by the user. The purpose of this paper is to describe the development and successful implementation of a heuristic-based scheduling software system that has been developed for a major Portuguese TV station.

Abstract
In this work, we address investment decisions in production systems by using real options. As is standard in literature, the stochastic variable is assumed to be normally distributed and then approximated by a binomial distribution, resulting in a binomial lattice. The methodology establishes a discrete-valued lattice of possible future values of the underlying stochastic variable (demand in our case) and then, computes the project value. We have developed and implemented stochastic dynamic programming models both for fixed and flexible capacity systems. In the former case, we consider three standard options: the option to postpone investment, the option to abandon investment, and the option to temporarily shut-down production. For the latter case, we introduce the option of corrective action, in terms of production capacity, that the management can take during the project by considering the existence of one of the following: (i) a capacity expansion option; (ii) a capacity contraction option; or (iii) an option considering both expansion and contraction. The full flexible capacity model, where both the contraction and expansion options exist, leads, as expected, to a better project predicted value and thus, investment policy. However, we have also found that the capacity strategy obtained from the flexible capacity model, when applied to specific demand data series, often does not lead to a better investment decision. This might seem surprising, at first, but it can be explained by the inaccuracy of the binomial model. The binomial model tends to undervalue future decreases in the stochastic variable (demand), while at the same time tending to overvalue an increase in future demand values.

Abstract
In this article we address the problem of determining how a structured formation of autonomous undistinguishable agents can be reorganized into another, eventually non-rigid, formation based on changes in the environment, perhaps unforeseeable. The methodology can also be used to correctly position the agents into a particular formation from an initial set of random locations. Given the information on the agents current location and the final locations, there are n! possible permutations for the n agents. Among these, we seek one that minimizes a total relative measure, e.g. distance traveled by the agents during the switching. We propose a dynamic programming methodology to solve this problem to optimality. Possible applications can be found in surveillance, damage assessment, chemical or biological monitoring, among others, where the switching to another formation, not necessarily predefined, may be required due to changes in the environment.