BDE2016

1. Responding to your environment

2. Predictive Analytics
UREASON | Bigdata Expo September 2016

3. UREASON
Active in: Process Industry, Telecom, Smart Grid, Smart Cities
since 2001
Vast Experience: Big Data, IoT, AI, Fault Management,
Predictive Analytics, Predictive Maintenance
A.I. Technology House
Recognized by Gartner as comprehensive supplier for Event
Stream Processing/Complex Event Processing technology
Proven track record with customers in wide variety of industry
– general theme: reason over large volumes of data to reduce
business uncertainty
Known as Innovator – from Concept to Feasibility and Roll-out
Main offices in the Netherlands (Delft) and the UK
(Maidenhead), sales offices in France and Germany
(c) - UREASON3

4. Operational Intelligence
Active in Operational Intelligence, providing real-time insights, reducing risks and/or cost
(c) - UREASON4
Industry Application
area
Results
Power
generation
Outage
prevention
Cost reduction
Chemical Gas leak
detection
24/7
monitoring,
increased
safety
Drinking water Pollution
assessment
Pollution
spread and risk
Oil & Gas ESP failure
prediction
Early insights
Power
generation
Theft detection Indication
Insurance Loyalty Early tipping
risk
identification

5. We Entered Into the World of Real-Time Predictive Analytics
(c) - UREASON5

6. Revolution(s)
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http://pages.experts-exchange.com/processing-power-compared/

7. Revolution(s)
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1.00E-06
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
MemoryPrice($/MB)
Year
Historical Cost of Computer Memory and Storage
Flip-
Flops
Core
ICs on
boards
SIMMs
DIMMs
Big
Drives
Floppy
Drives
Small
Drives
Flash
Memory
SSD
1.00E-06
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Price($/MB)
Year
Disk Drive Cost with TIme
Floppy Disk
Drives
Mainframe Drives
Small Disk Drives
Flash Memory
http://www.jcmit.com/memoryprice.htm

8. Revolution(s)
(c) - UREASON8
World Economic Forum, January 2015, Industrial Internet of Things: Unleashing the Potential of Connected Products and Services
“If you can’t measure it, you can’t manage it.”

9. ‘New’ Business Models Appearing
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Source TechWorld

10. Technology Storms
MIT Technology Review
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2016 2015 2014
Immune Engineering Magic Leap Agricultural Drones
Precise Gene Editing in Plants Nano-Architecture Ultraprivate Smartphones
Conversational Interfaces Car-to-Car Communication Brain Mapping
Reusable Rockets Project Loon Neuromorphic Chips
Robots that Teach Each Other Liquid Biopsy Genome Editing
DNA App Store Megascale Desalination Microscale 3-D Printing
SolarCity’s Gigafactory Apply Pay Mobile Collaboration
Slack Brain Organoids Oculus Rift
Tesla Autopilot Supercharged Photosynthesis Agile Robots
Power from the Air Internet of DNA Smart Wind & Solar Power

11. Recipe for Succes?
New Business Models + New
Technologies  Innovation
Failure is the foundation for
innovation:
• Regulatory-compliance: inspection data,
maintenance data and real-time data
allows you to determine in real-time if
your assets/machines are compliant to
regulations. The complexity of
determining this is diverse – legal,
operational knowledge, history, location
all play a role. A system was developed
that was able to track all of this data and
provide advisories to operational
personnel. This provided better insights
and reduced risk.
(c) - UREASON11

12. Recipe for Succes?
• Intelligent Traffic Notification: debottlenecking traffic by
evenly distributing the traffic across the road network.
Reducing carbon footprint and enabling delivery route
optimization for delivery and transportation companies.
• Detecting gas and bio attacks early: using proven sensor
technology and mobile phones to create a mess network of
human sensors
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13. Predictve Analytics -Indicators for Failure
Data Quality!!?
Pure Algorithmic Approach?? - Searching for the needle in the (moving) haystack of data
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Source: Dilbert.com

14. Predictve Analytics -Indicators for Failure
Correlation or Causation?
Predict without Classification?
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15. Zoom In: OSS Overload in the NOC
Vast reduction of the quantity of alarms, improving
situational awareness, automated actions
(c) - UREASON15

16. Overview
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TEMIP
EMS – Streaming Alarm Data
Cross-Silo Integration
Real-time Weather Data
Maintenance Data
Customer/SLA Requirements
Business/Performance Requirements
Alarm Expert
VendorParser
UserAction/Rules
Rules
Engine
Automated
Actions
Alarm Expert Web Portal
Alarm Rules and Actions
Network
Equipment
Network
Equipment
Network
Equipment
Synchronization Logic Enriched Alarms
Grouped Alarms
Reduced Alarms

17. Deployment Realized
(c) - UREASON
TestSystemLiveSystem
17
Nokia
Operational Context
Huawei
Operational Context
HP TeMIP Instance
Ericsson
Operational Context
Alarm Expert
Ericsson
Alarm Expert
Nokia
Alarm Expert
Huawei
2G/3G/LTE 2G/3G/LTE2G/3G/LTE 2G/3G/LTE 2G/3G/LTE 2G/3G/LTE 2G/3G/LTE
ET SW NOFI LISW FI

18. Zoom-In: Predictive Asset Failure Detection
(c) - UREASON
(Sequential PAttern Discovery using Equivalence)
Real-time
Predictive Failure
Actions/Advice
Alarm
Expert
Asset
Event/Alarms
Breakdowns/
Delays
Real-time Asset
Event/Alarms
Real-time
Predictive
Maintenance
Pattern
Rules
Actions
Candidate Patterns
Event Pattern
Detection Agent
Using pattern mining and rule induction techniques, turn alarm data into rules that can
reduce the amount of information presented to operators and predict conditions
MSAP, Iteration 2
Raw Results
MSAP, Iteration 3
Raw Results
MSAP, Iteration 1
Raw Results
cSPADE
Joined
MSAP
Iterations
MSAP
Iteration 1
MSAP
Iteration 3
MSAP
Iteration 2
18

19. Steps Towards a Successful Predictive Analytics Program
Rules of engagement:
1. Clear business requirements and understanding
2. Take a systematic approach to data management
3. Select the right toolbox(es) for the job at hand
4. Involve the users early on
5. Data pruning and aggregation is key but useless without understanding state
6. Model training & validation is iterative as such difficult to fit traditional project planning
– take a RAD approach
(c) - UREASON19

20. What Next?
Identify where your organisation can benefit from Predictive Analytics
Keep in mind:
- More data ≠ More Insight
- Insight does not mean Value
UREASON can support you in
- Big Data & Advanced Analytics Training & Awareness
- Proof of Concept Advanced Analytics (Predictive & Prescriptive)
- Technology Selection
- Your Advanced Analytics projects – Technology & Knowledge
(c) - UREASON20

21. (c) - UREASON21
Responding to your environment
Contact
UREASON International BV
Drie Akersstraat 11
2611 JR, Delft
The Netherlands
Telephone:
General: +31 85 273 49 20
Fax: +31 85 273 49 29
Email:
General: info@ureason.com
Support: support@ureason.com