A first attempt on online data stream classifier using context

Abstract

The big data is characterized by 4Vs (volume, velocity, variety, and variability). In this paper we focus on the velocity, but actually it usually comes together with volume. It means, that the crucial problem of the contemporary data analytics is to answer the question how to discover useful knowledge from fast incoming data. The paper presents an online data stream classification method, which adapts the classification with context to recognize incoming examples and additionally takes into consideration the memory and processing time limitations. The proposed method was evaluated on the real medical diagnosis task. The preliminary results of the experiments encourage us to continue works on the proposed approach.