Presented By O'Reilly and Cloudera
Make Data Work
December 1–3, 2015 • Singapore

Telecom conference sessions

9:00am–12:30pm Tuesday, 12/01/2015
Juliet Hougland (Cloudera), Sandy Ryza (Cloudera)
In this half-day tutorial, attendees will get a taste of how large-scale data science techniques and technologies developed for the consumer internet can be applied in the world of Telecom.
4:00pm–4:40pm Thursday, 12/03/2015
Slides:   1-PPT 
The fast evolution of services and mobile terminals combined with the aggressive competition between mobile operators is driving a continuous upgrade of the radio access network (RAN). This upgrade process is expensive and time consuming, and it scales with the number of base stations. This talk stresses the importance of the customer and proposes a new methodology for an efficient RAN upgrade.
11:00am–11:40am Thursday, 12/03/2015
Albert Bifet (Télécom ParisTech), Silviu Maniu (Huawei)
Slides:   1-PDF 
Real-time analytics are becoming increasingly important to telecommunication operators due to the large amount of data that flows through their networks. Drawing from our experience at Huawei, we present StreamDM, a new open source data mining and machine learning library on top of Spark Streaming. We will present its implemented advanced methods, and demonstrate its ease of use and extensibility.
11:00am–11:40am Wednesday, 12/02/2015
Amy Shi-Nash (Singtel)
This talk will broach the topic of how DataSpark has created an innovative way of understanding people and what is important to them, by leveraging advanced data science and the wealth of data in an aggregated manner, while adhering to high standards of data privacy.
11:50am–12:30pm Wednesday, 12/02/2015
Thomas Holleczek (Singtel)
We present a traffic measurement system that monitors subway and expressway traffic from telco location data.
4:50pm–5:30pm Wednesday, 12/02/2015
Arshak Navruzyan (
Like most large internet sites, Telecom networks are constantly under attack by highly sophisticated fraudsters. Historically, carriers have tried to isolate fraudulent behavior through complex rules. However, increasingly there is a need to use machine learning algorithms that can keep up with the changing face of Telecom fraud.