Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

Detecting anomalies in the real world

13:30–14:00 Wednesday, 1/06/2016
Hardcore data science
Location: Capital Suite 4 Level: Advanced
Tags: real-time, iot
Average rating: ****.
(4.50, 4 ratings)

Prerequisite knowledge

Attendees should have a basic understanding of machine learning.


Anomaly detection as a data science technique is used in many applications, from the IoT to finance. With the rise of the industrial Internet and the explosion of sensor data, businesses from transport to manufacturing are keen to develop predictive maintenance. (Another key area where anomaly detection is important is identifying fraud in finance and within the social benefit system.)

There are, however, a number of challenges when applying anomaly detection that are hindering progress. For a start, anomaly detection is a challenging problem by definition: defining and distinguishing between “normal” and an “anomaly” is often part of the problem statement. An anomaly is a relatively rare event and, hence, suffers from the accuracy paradox. Moreover, what is a good measure of success? Because of the nature of the problem, if the model misses all the anomalies, it will still be very accurate. The vastly different data types and preprocessing required, as well as the complex ensemble machine-learning methods needed, prove an additional challenge.

Alessandra Staglianò illustrates these challenges through two very different use cases—the IoT and fraud detection—and explains how to overcome them. You’ll explore the differences and the similarities of the two industries and learn how to set up a framework to solve anomaly detection in these situations.

Photo of Alessandra Staglianò

Alessandra Staglianò


Alessandra Staglianò is a data scientist who has worked on multiple complex projects. In addition to various machine-learning techniques, Alessandra’s expertise is in extracting relevant information from noisy and redundant data. Her former research work has been published in a variety of journals. Alessandra holds a PhD in computer science specializing in machine learning and machine vision.

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kouami barnabas
3/08/2016 2:54 BST

hello. I defended my master thesis on outlier detection in wireless sensor networks data. I explored one-class SVM models and elliptical envelop model of SKLEARN util. By designing a new method, I overcame the weakness of elliptical envelop when normal data are Gaussian distributed but in two clusters. Please, i would like to publish my results. Can you give me links for possibles active conferences or journals where i can publish that work? I’m also looking a director for my PHD always in the field of anomaly detection. Please, can you supervise my phd or put me in a project in that field where i can easily fulfil my phd?