Regression on evolving multi-relational data streams
Elena Ikonomovska, Sašo Džeroski · 2011
In the last decade, researchers have recognized the need of an increased attention to a type of knowledge discovery applications where the data analyzed is not finite, but streams into the system continuously and endlessly. Data streams are ubiquitous, entering almost every area of modern life. As a result, processing, managing and learning from multiple data streams have become important and challenging tasks for the data mining, database and machine learning communities. Although a substantial body of algorithms for processing and learning from data streams has been developed, most of the work is focused on one-dimensional numerical data streams (time series) or a single multi-dimensional data stream. Only few of the existing solutions consider the most realistic scenario where data can be incomplete, correlated with other streams of information and can arrive from multiple heterogeneous sources.