document: 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 .
doc_bio_tags: O B B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I I O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O