Can and How Telco benefit from big data analytics?
Understanding 5G, Internet of Things, and Big Data.
Big Data Analytics for 5G networks offers an opportunity to gain a better picture of operations and customers, and to maximise profits and user satisfaction.

Achieving and managing a pro-active core network with advanced packet priority and routing management, and exploiting SDN technology will maximise the global throughput and minimise jitter and latency.
The use of the SDN technology is linked to the use of Big Data.
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Big Data Analytics - A use case for 5G deployment
1. Big Data and
Analytics Use
Cases for the 5G
deployment
We are drowning in data, but starving for
knowledge!
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2. Authors
• Dimitris Tsaptsinos
o Kingston University, UK
o Squares on Blue, France
• dtsaptsinos@kingston.ac.uk
• Ioannis Papadopoulos
o Squares on Blue, France
• Arthur Dupont
o Telecom Nancy, University of Lorraine, France
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3. Proms in the park
• 13 September 2014
• 50000 people
• Spent most of the time with E and GPRS unable to
check football scores
• Blamed my network provider, the English weather
and Terry Wogan!
• Not a good experience…
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4. Can & How Telco
benefit from big data
analysis?
And that is the question
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6. Pressure on the Network
• 10000 times more traffic
o Cloud computing
o HQ streaming
o M2M
• Zero latency and response time
o Real time applications
• 100 times more devices
o IoE
• Up to 10 Gbps
o For cloud computing
• At least 1Gbps
o For HQ streaming etc
• Zero-second switching
o To ensure seamless continuity of service
• Energy consumption
o To ensure devices’ batteries durability
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7. Is this the future?
• “Internet of things”.
o WWWW - World Wide Wireless Web.
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8. Internet of Things
• direct device-to-device transmission and
participation (the BitTorrent of mobile
communication),
• higher number and diversified devices connected
at the same time and communicating to each
other,
• a pervasive network where the user is connected to
more than just the “Provider’s network”
• and also users could be potential cooperative
nodes in the data transmission (BitTorrent like).
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9. Understanding of 5G
• Definitely not a linear extrapolation of 1G,
2G, 3G, 4G...
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10. Data Explosion
• Regardless of the kind of industry we face a huge
stream of data.
• Data needs to be transformed to information
• Information to be translated to new services
We have produced more data in the past four years than in all the time after the
Bing Bang
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11. Big Data Definition
• Not a single standard definition…
o Big Data is data whose scale, diversity and
complexity require new architectures,
techniques, algorithms, and analytics to manage
it and extract value and hidden knowledge from
it…
o Defined through the four dimensions
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12. Sharepoint
Text
Forums
Weather
Sensor
Location
Financial
records
HR records
Order records
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13. 4G/5G/BDA
• Big Data Analytics is relevant now
o but ever more importantly for 5G because of the nfold increase in data.
• The same principles remain analysing 4G data but
more challenging when it comes to the explosion of
data with 5G.
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14. Todays Model
• Dump Pipes for service providers
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15. Todays Modelling
• Based on
o Historical data
o Batched data
o Structured data
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16. Tomorrows Model
• Smart pipes
Knowing Dimitris
• 079345678
• 192.168.20.45
• Male
• Lives in London
• Uses Nokia
Understanding
Dimitris
• Supports AEK
• Researches
villas in South
France
• Listens to
Greek music
Supporting
Dimitris
• Located half a mile
from a music store
that sells Greek
music
• Top recommended
villas in South of
France
Personalise targeted products and marketing offers
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17. Tomorrows Modelling
• Based on (in addition to…)
o Real time data
o Unstructured data
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18. BDA Benefits
• Operational Support Systems for 5G
o Building a suitable and flexible 5G network
o Caching popular data
o Smart core network
• Business Support Systems for 5G
o Intelligent marketing
o Fraud intelligence
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19. Building a suitable and
flexible 5G network
• Deploying and maintaining the network is one of
the costliest operations for operators of cellular
networks.
• Antennas have to be placed in an optimal way to
address present and future needs.
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20. Challenge
• An operator would benefit from these technologies
only if the antennas are well placed.
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21. Analysing data
• Long-term trends
o to determine the evolving needs for the network
• Seasonal trends
o to determine where and when consumptions peaks are
expected so that node-drones are placed accordingly.
• Short-term needs
o in order to deploy accordingly node-drones or mobile BTS
to absorb consumption peaks
• Behavioural and opinion mining
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22. Send the drones
• With a Big Data Analytics solution one can predict
how the needs in bandwidth change from one
place to another throughout the day, week, month,
etc.
• Send radio access node-drones to the relevant
locations and where antennas should be directed,
so that consumption peaks are absorbed smoothly
without compromising the user experience of the
customers.
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23. Caching popular data
• With the raise of social networks, entertainment
applications, and web sites, mobile users increasingly
consume images and streaming content such as video
and music.
• Trends and recommendations cause some content
pieces to become viral.
• Every time a piece of content is requested, packets
have to travel the entire network to reach the servers.
Such data traffic often causes network congestion.
• Predicting popular requests, and pre-caching
information close to the end user, will reduce
significantly backhaul traffic and peak data traffic.
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24. Example
• Imagine a tabloid newspaper revealing a shocking
affair involving a celebrity popular among
teenagers in the UK.
• Big Data Analytics algorithms would identify that
young people in big cities will increasingly request
this particular piece of content.
• At a given time, knowing where the majority of
teenagers are located in the city of London, the
newspaper’s content can be cached in specific
BTS.
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25. Extend and improve Operators BSS
• With data from customers and from the
network, Big Data & Analytics technologies
can be used to cluster customers in different
categories.
• A specific set of services and pricing can be
offered to each group
o propose customised contracts for users and improve
customer fidelity.
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26. Example
• Let’s imagine that it is hot and sunny, a customer is
wearing a connected cloth that identifies
dehydration then while s/he walks near an
operator’s partner bar the operator can send a text
message to the customer to offer him a discount on
a drink for this specific bar.
• Alternatively, a discount text for the restaurant near
the office on a Friday night?
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27. Survey Answers
• The two main benefits of Big Data Analytics would
be:
• Maximisation of the global throughput of the
network using a pro-active core network, with
advanced packet priority and routing
management, exploiting SDN technology.
• Minimising jitter and latency using a pro-active core
network, with advanced packet priority and routing
management, exploiting SDN technology.
• It seems that the use of the SDN technology is linked
to the use of Big Data.
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28. Essential Technology
• Parallel Processing
• Real-time processing
• Predictive analytics – existing and new ways of
crunching data
o Structured and Unstructured
• Visualisation algorithms
• Distributed file systems
o Hadoop
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29. Research Problems
• The research problems are related to
o anonymisation of data,
o processing extreme large amount of data in real time,
o finding balance between local processing and transport of data etc.
• From my experience 75% of our time will be taken
by understanding our data (cleaning, reducing,…)
and the rest for running and testing applications.
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30. BDA offers an opportunity to gain a
beUer picture of operations and
customers, and to
maximise profits and user satisfaction
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