Motivations and Topics

Many sources produce data continuously. Examples include customer click streams, telephone records, large sets of web pages, multimedia data, and sets of retail chain transactions. These sources are called data streams. If the process is not strictly stationary (as most of real world applications), the target concept could gradually change over time. This is an incremental task that requires incremental learning algorithms that take drift into account. Data streams are increasingly important in the research community, as new algorithms are needed to process this streaming data in reasonable time. This is an important issue for different research areas like data mining, machine learning, OLAP, databases, etc. Many researches in these areas are designing new approaches or adapting some of the traditional algorithms to data streams.