A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. ...

In this paper we show how frequent sequence mining (FSM) can be applied to data produced by monitoring distributed enterprise applications. In particular we show how we applied FSM to run-time paths to highlight repeating sequences of interest by using alternative support counting techniques. We show how the patterns identified, can be used to highlight design flaws in enterprise applications. ...

By analyzing historical data sequences and identifying relations between the occurring of data items and certain types of business events we have opportunities to gain insights into future status and thereby take action proactively. This paper proposes a new approach to cope with the problem of prediction on data sequence characterized by multiple dimensions. The proposed relation mining approa...

The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four cat...

Learning of temporal sequences is a topic of research in such different areas as speech and other temporal pattern recognition as well as motor control (for a survey see e.g. Mozer, 1993). In this paper an approach is presented which is particularly suitable for motor control due to the fact that it does not only reproduce temporal sequences in exactly the way they where learned but it is able ...

Let a textstring T of n symbols from some alphabet E and an integer m < n be given. A pattern P of length mover E is sought such that P minimizes (alternatively, maximizes) the total number of pairwise character mismatches generated when P is compared with all mcharacter substrings of T. Two additional variants of the problem are obtained by adding the constraint that P be (respectively, not be...

Sequential pattern mining is very important because it is the basis of many applications. Although there has been a great deal of effort on sequential pattern mining in recent years, its performance is still far from satisfactory because of two main challenges: large search spaces and the ineffectiveness in handling dense datasets. To offer a solution to the above challenges, we have proposed a...

Candida albicans is a medically important pathogen, and recognition by innate immune cells is critical for its clearance. Although a number of pattern recognition receptors have been shown to be involved in recognition and phagocytosis of this fungus, the relative role of these receptors has not been formally examined. In this paper, we have investigated the contribution of the mannose receptor...

We formally deene a class of sequential pattern matching algorithms that includes all variations of Morris-Pratt algorithm. For the last twenty years it was known that the complexity of such algorithms is bounded by a linear function of the text length. Recently, substantial progress has been made in identifying lower bounds. We now prove there exists asymptotically a linearity constant for the...

Sequential pattern mining deals with data represented as sequences (a sequence contains sorted sets of items). Compared to the association rule problem, a study of such data provides “inter-transaction” analysis (Agrawal & Srikant, 1995). Applications for sequential pattern extraction are numerous and the problem definition has been slightly modified in different ways. Associated to elegant sol...