Processing and Mining Complex Data Streams

Data mining aims at discovering valid, novel and potentially useful patterns from data. Over last two decades, data mining and the related, older discipline of machine learning have shown tremendous progress and become ones of the main sub-fields of computer science. Novel research problems have been identified, many innovative methods have been introduced and the number of their applications in various areas has been increasing relevantly. As a result, both data mining and machine learning have become powerful tools for many areas, such as medicine, biology, economy, finance, social sciences and others. Nevertheless, many of current approaches assume processing static and simple (usually tabular) of data. Such kinds of data occur in most of popular software systems and they are typically easy to obtain from relational databases. However, this data model appears to be too restrictive as modern information technologies give access to massive, complex and dynamic data, often in a form of data streams.