A Constraint-Based Algorithm for Mining Temporal Relational Patterns

In this article, we consider a new kind of temporal pattern where both interval and punctual time representation are considered. These patterns, which we call temporal point-interval patterns, aim at capturing how events taking place during different time periods or at different time instants relate to each other. The datasets where these kinds of patterns may appear are temporal relational databases whose relations contain point or interval timestamps. We use a simple extension of Allen’s Temporal Interval Logic as a formalism for specifying these temporal patterns. We also present the algorithm MILPRIT* for mining temporal point-interval patterns, which uses variants of the classical levelwise search algorithms. In addition, MILPRIT* allows a broad spectrum of constraints to be incorporated into the mining process. An extensive set of experiments of MILPRIT* executed over synthetic and real data is presented, showing its effectiveness for mining temporal relational patterns. INtrODUctION AND MOtIVAtION The problem of discovering sequential patterns in temporal data has been studied extensively in the past years (Pei et al., 2004; Srikant & Agrawal, 1996; Zaki, 2001), and its importance is fully justified by the great number of potential application domains where mining sequential patterns appears as a crucial issue, such as financial