Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. One important problem is mining data streams in extremely large databases (e.g. 100 TB). Satellite and computer network data can easily be of this scale. However, today’s data mining technology is still too slow to handle data of this scale. In addition, data mining should be a continuous, online process, rather than an occasional one-shot process. Organizations that can do this will have a decisive advantage over ones that do not. One particular instance is from high speed network traffic where one hopes to mine information for various purposes, including identifying anomalous events possibly indicating attacks of one kind or another. A technical problem is how to compute models over streaming data, which accommodate changing environments from which the data are drawn. This is the problem of “concept drift” or “environment drift.” This problem is particularly hard in the context of large streaming data. How may one compute models that are accurate and useful very efficiently? For example, one cannot presume to have a great deal of computing power and resources to store a lot of data, or to pass over the data multiple times. Hence, incremental mining and effective model updating to maintain accurate modeling of the current stream are both very hard problems.

Data Stream, Data Stream Mining, Concept Drift/Environment Drift

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