Mining and Learning from Data Streams


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By :Shima Rashid 

Abstact: The term "streaming" is used to describe continuous, never-ending data streams with no beginning or end, that provide a constant feed of data that can be utilized/acted upon without needing to be downloaded first.

Similarly, data streams are generated by all types of sources, in various formats and volumes. From applications, networking devices, and server log files, to website activity, banking transactions, and location data, they can all be aggregated to seamlessly gather real-time information and analytics from a single source of truth.

Learning from the data stream is an important aspect. Datastream mining is applying data mining algorithms to one or several data streams. For Data stream mining, we need Data-Stream Management System to manage data. Also, we face lots of challenges in data stream processing like Concept-drift, Concept-evolution, Feature Evolution, … . All these concepts and methods of making decisions in the datastream process will be discussed in the workshop.