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Data stream mining and modeling is a recent methodology that deals with the analysis of potentially large volumes of ordered sequences of data samples. Sensor networks, e-mails, online transactions, network traffic, weather forecasting, health monitoring, industrial process monitoring, and social networks are just some of the most common sources of this kind of data. Stream data arrive continuously. They dynamically change over the time, and need to be processed as soon as they arrive, in a finite amount of time. The idea is to capture the essence of the information within the data, and represent it in the parameters and structure of a model. Thus, special-purpose evolving data analysis methods, which are able to identify patterns in data in quite a real time, are needed to address major challenges such as nonstationarity (concept change) and large datasets (Big data). |
