Posters Galore: 'Detecting Emergent Phenomena within Streaming Data'

Edward Austin
Lancaster University

Edward Austin, Lancaster University

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Throughput data is a measure of the volume of internet traffic passing through points on the BT network. Due to consumer behaviour over the course of a day, this data takes an expected shape; put simply, less data is streamed in the early hours of the morning, than in the middle of the working day. Deviations from this expected shape, however, are indicators of faults, outages, or unexpected customer behaviour taking place on the network. The rapid detection of these deviations is critical as it allows maintenance to be performed to fix the problem. This benefits the customer, and in turn enhanced BT revenue and reputation. 

This work explores how mathematical tools can be used to model daily throughput data with smooth curves, and then how differential equations can be used to model the expected shape the throughput data takes. A test is then presented for the detection of deviations from this shape in real time. 

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