Quality-driven resource-adaptive data stream mining?

Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work under virtually any dynamic resource constraints. Few approaches exist for stream mining algorithms that are capable to adapt to given constraints, and none of them reflects from the resource adaptation to the resulting output quality. In this paper, we propose a general model to achieve resource and quality awareness for stream mining algorithms in dynamic setups. The general applicability is granted by classifying influencing parameters and quality measures as components of a multiobjective optimization problem. By the use of CluStream as an example algorithm, we demonstrate the practicability of the proposed model.

Publisher

IEEE / ACM

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

Data Science Institute (Scholarly Articles)