Abstract. Previous studies on mining sequential patterns have focused on temporal patterns specified by some form of propositional temporal logic. However, there are some interesting sequential patterns, such as the multi-sequential patterns, whose specification needs a more expressive formalism, the first-order temporal logic. In this paper, we extend a well-known user-controlled tool, based on regular expressions constraints, to the multi-sequential pattern context. This specification tool enables the incorporation of user focus into the multi-sequential patterns mining process. We present MSP-Miner, an Aprioribased algorithm to discover all frequent multi-sequential patterns satisfying a user-specified regular expression constraint. 1.

Abstract. Thanks to an important research effort the last few years, inductive queries on local patterns (e.g., set patterns) and complete solvers which can evaluate them on large data sets have been proved extremely useful. The more we use such queries on real-life data, e.g., biological data (and thus intrinsically dirty and noisy), the more we are convinced that inductive queries should return fault-tolerant patterns. In this work, we consider user-defined constraints for a declarative specification of faulttolerance. We discuss the design of such constraints on bi-sets extracted from Boolean data sets. Our starting point is the fundamental limitation of formal concept discovery (i.e., closed set mining) from noisy data and we propose a constraint-based mining approach for relevant faulttolerant bi-set mining. Formalizing three recent proposals, our framework enables a better understanding of the needed trade-off between extraction feasibility, completeness, relevancy, and ease of interpretation of these fault-tolerant patterns. An original empirical evaluation on both synthetic and real-life medical data is given. It enables a comparison of the various proposals and it motivates further directions of research. 1

Abstract. The paradigm of pattern discovery based on constraints has been recognized as a core technique in inductive querying: constraints provide to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. In our previous work [5], we analyzed such limitations and showed how they flow out from the same source: the fact that in the classical constraint-based mining, a constraint is a rigid boolean function which returns either true or false. To overcome such limitations we introduced the new paradigm of pattern discovery based on Soft Constraints, and instantiated our idea to the fuzzy soft constraints. In this paper we extend the framework to deal with probabilistic and weighted soft constraints: we provide theoretical basis and detailed experimental analysis. We also discuss a straightforward solution to deal with top-k queries. Finally we show how the ideas presented in this paper have been implemented in a real Inductive Database system. 1

Abstract. One of the unresolved problems faced in the construction of intelligent tutoring systems is the acquisition of background knowledge, either for the specification of the teaching strategy, or for the construction of the student model, identifying the deviations of students ’ behavior. In this paper, we