On string classification in data streams

String data has recently become important because of its use in a number of applications such as computational and molecular biology, protein analysis, and market basket data. In many cases, these strings contain a wide variety of substructures which may have physical significance for that application. For example, such substructures could represent important fragments of a DNA string or an interesting portion of a fraudulent transaction. In such a case, it is desirable to determine the identity, location, and extent of that substructure in the data. This is a much more difficult generalization of the classification problem, since the latter problem labels entire strings rather than deal with the more complex task of determining string fragments with a particular kind of behavior. The problem becomes even more complicated when different kinds of substrings show complicated nesting patterns. Therefore, we define a somewhat different problem which we refer to as the generalized classification problem. We propose a scalable approach based on hidden markov models for this problem. We show how to implement the generalized string classification procedure for very large data bases and data streams. We present experimental results over a number of large data sets and data streams.