
From Sensor Networks to Customer Clicks: How Online Learning Makes Sense of Real-Time Data
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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 strea...
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.