[MapR Talk]How to Find What You Didn't Know to Look For, Practical Anomaly Detection

Document created by aalvarez Employee on Nov 30, 2015Last modified by aalvarez Employee on Dec 7, 2015
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Overview

In an effort to help grow organic communities interested in new technologies, MapR speakers around the world provide technical talks on numerous topics. Please browse our MapR Talks Directory  to learn how to search and request a talk in seconds.

 

Abstract

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

 

Location Availability & Request Link

North America. Please refer to theMapR Talks Directory  for specific countries.

You can request this talk here: Speaker Request

 

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