The aim of this PhD program is the study of algorithms for learning histograms, with the capacity of representing continuous high-speed flows of data and dealing with the current problem of change detection on data streams.

In many modern applications, information is no longer gathered as finite stored data sets, but assuming the form of infinite data streams. As a large volume of information is produced at a high-speed rate it is no longer possible to use memory algorithms which require the full historic data stored in the main memory, so new ones are needed to process data online at the rate it is available. Moreover, the process generating data is not strictly stationary and evolves over time; so algorithms should, while extracting some sort of knowledge from this incessantly growing data, be able to adapt themselves to changes, maintaining a representation consistent with the most recent status of nature.

In this work, we presented a feasible approach, using incremental histograms and monitoring data distributions, to detect concept drift in data stream context.