Adaptivity in Data Stream Mining

In recent years data streams became a ubiquitous source of information, and thus stream mining emerged as a new field in database research. Due to the inherently dynamic nature of data streams, stream mining algorithms benefit from being adaptive to changes in the properties of a data stream. In addition, when stream mining is done in a dynamic environment like a data stream management system or a sensor network, stream mining algorithms also pro t from being adaptive to the changing conditions in this environment. This paper investigates two kinds of adaptivity in data stream mining. First, a model for quality-driven resource adaptive stream mining is developed.