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Anomaly detection is the art of automating surprise. To do this, we have to be able to define what we mean by normal and recognize what it means to be different from that. The basic ideas of anomaly detection are simple. You build a model and you look for data points that don’t match that model. The mathematical underpinnings of this can be quite daunting, but modern approaches provide ways to solve the problem in many common situations.
We will describe these modern approaches with particular emphasis on several real use-cases including:
a) rate shifts to determine when events such as web traffic, purchases or process progress beacons shift rate.
b) time series generated by machines or biomedical measurements.
c) topic spotting to determine when new topics appear in a content stream such as Twitter.
d) network flow anomalies to determine when systems with defined inputs and outputs act strangely.
In building a practical anomaly detection system you have to deal with practical details starting with algorithm selection, data flow architecture, anomaly alerting, user interfaces and visualizations.
We will show how to deal with each of these aspects of the problem with an emphasis on realistic system design.
KEYWORDS: Data Science, Use Case
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North America. Please refer to theMapR Talks Directory for specific countries.
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