On Classification of High-Cardinality Data Streams

The problem of massive-domain stream classification is one in which each attribute can take on one of a large number of possible values. Such streams often arise in applications such as IP monitoring, super-store transactions and financial data. In such cases, traditional models for stream classification cannot be used because the size of the storage required for intermediate storage of model statistics can increase rapidly with domain size. Furthermore, the one-pass constraint for data stream computation makes the problem even more challenging. For such cases, there are no known methods for data stream classification. In this paper, we propose the use of massive-domain counting methods for effective modeling and classification. We show that such an approach can yield accurate solutions while retaining spaceand time-efficiency. We show the effectiveness and efficiency of the sketch-based approach.