Abstract. Pruning provides an important tool for control of nondeterminism in Prolog systems. Current Tabled Prolog systems improve Prolog's evaluation strategy in several ways, but lack satisfactory support for pruning operations. In this paper we present an extension to the evaluation mechanism of Tabled Prolog to support pruning. This extension builds on the concept of demand to select tables to prune. In particular, we concentrate on systems based on SLG resolution. A once operator is described, which approximates demand-based pruning, providing for an efficient implementation in the XSB system.

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Data mining techniques are mainly focused on supporting the decision makers in a specific organization. Student attrition is a common phenomenon that worries public and private universities, which are affected financially and socially. Several studies have addressed this issue. However, they have mainly focused on academic, social, demographic, and economic aspects. In this paper, we propose a method for analyzing academic desertion in the context of a Systems and Computing Engineering undergraduate program by providing a view of this issue from a KDD (knowledge discovery in databases) perspective and using techniques for identifying students’ behavioral patterns. Unlike other proposals, we also consider variables provided by the BADyG test. This proposal is important because it will support higher education institutions in decision-making and creating action plans to reduce the high rate of student attrition.