Unlocking the Power of Streaming Data: Online Learning for Diverse Applications

Data streams are defined as large sequences of data, gathered from sources such as sensor networks and customer click streams, that are possibly infinite and temporarily ordered [7, 22]. Instances in data streams arrive fast, either in batches of data, or instance-by-instance; each instance needs to be processed in a timely manner. Due to these characteristics, such as large amount of data and time constraints, tra-ditional static machine learning algorithms are unsuitable for direct use [7]. That is, techniques learning from data streams need to maintain their performance throughout the stream while limiting memory and processing time. Moreover, evolving or non-stationary data streams are susceptible to changes in the distribution of data, also known as concept drifts.