We consider online mining of correlated heavy-hitters (CHH) from a data stream. Given a stream of two-dimensional data, a correlated aggregate query first extracts a substream by applying a predicate along a primary dimension, and then computes an aggregate along a secondary dimension. Prior work on identifying heavyhitters in streams has almost exclusively focused on identifying heavy-hitters on a single dimensional stream, and these yield little insight into the properties of heavyhitters along other dimensions. In typical applications however, an analyst is interested not only in identifying heavy-hitters, but also in understanding further properties such as: what other items appear frequently along with a heavy-hitter, or what is the frequency distribution of items that appear along with the heavy-hitters. We consider queries of the following form: "In a stream S of (x, y) tuples, on the substream H of all x values that are heavy-hitters, maintain those y values that occur frequently with the x values in H ". We call this problem as CHH. We formulate an approximate Responsible editor: Bart Goethals. Electronic supplementary material The online version of this article (123 798 B. Lahiri et al. formulation of CHH identification, and present an algorithm for tracking CHHs on a data stream. The algorithm is easy to implement and uses workspace much smaller than the stream itself. We present provable guarantees on the maximum error, as well as detailed experimental results that demonstrate the space-accuracy trade-off.
REF introduced the notion of correlated heavy hitters and proposed an approximate solution based on the Misra-Gries algorithm.
Identifying correlated heavy-hitters in a two-dimensional data stream
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Computer Science