This paper addresses the discovery of sequential patterns in very large databases. Most of the existing algorithms use lattice structures in the space search that are very demanding computationally. The output of these algorithms generates a large number of rules. The aim of this work is to create a swift algorithm for the discovery of sequential patterns with a low time complexity. In this wor...

Many knowledge discovery (kdd) systems need to spend substantial amount of eeort to search for rules and patterns within large amount of data. After some natural evolutions, as a consequence of updates applied to their databases, these systems must update their previously discovered knowledge to reeect the current state of their databases. The straight forward approach of re-running the discove...

The Web service architecture promises to be highly dynamic. The idea is that a service requester can discover a needed service at runtime. Due to the fact that this discovery process relies on a semantic understanding of the offered services, it is presently performed manually at development time only. In this paper we propose to use graph transformation rules to describe the semantics of Web s...

We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association...