SDIL overview presentation from IBM Insight 2015, refer to http://sdil.de/

1. © 2015 IBM Corporation
Smart Data Innovation Lab:
From Big Data to Smart Data
Session: DPA-2135
Jan Erik Sundermann
Karlsruhe Institute of Technology
Plamen Kiradjiev
IBM Germany
October 28, 2015

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Therefore, no assurance can be given that an individual user will achieve results similar to those stated
here.
Please Note:
2

3. Biographies
Jan Erik Sundermann is research associate at Karlsruhe Institute of Technology’s Steinbuch Centre for
Computing. He is part of the team responsible for planning, deployment and operation of the SDIL
computing platform. Jan Erik has expertise in the field of scientific computing, distributed computing
and data analysis which he gained during his PhD studies and as a postdoctoral researcher in the field
of experimental particle physics participating in experiments at SLAC and CERN.
3
Jan Erik Sundermann
Research Associate
Karlsruhe Institute of Technology
Steinbuch Centre for Computing
Plamen Kiradjiev is Executive Architect at IBM leading a TechSales team focused on Industrie 4.0 ,
delivering IT solutions for machine constructors and OEMs, as well as partnering with automation
providers and integrators. He has 20 years experience in the IT business – software architectures,
business development, pilot implementations. As the IBM Ambassador for SDIL, Plamen represents
IBM as one of the Core Partners in the SDIL initiative.
Plamen Kiradjiev
Executive Architect
Industrie 4.0 Core Tech Team Lead
IBM Ambassador @SDIL

4. Agenda
4
2
Smart Data Innovation Lab – Why & What
KIT SCC and its role in SDIL
1
3 IBM‘s contribution: Watson Foundation on POWER
4 First projects and experiences

5. Steinbuch Centre for Computing
Smart Data Innovation Lab (SDIL):
A joint research platform for Big Data
• Smart Data Innovation = generate knowledge from data
• SDIL: research platform from science and industry
• Aim: joint generation of added value in innovative application fields
 With new algorithms and methods
 On the basis of securely handled data
 In the framework of well-defined projects
Supported by

6. SDIL: WIN-WIN-WIN between
1. Industry:
 Lower threshold to experiment
with Big Data analytics
 Access to cutting-edge
research and technology
 Leverage Smart Data for
tangible business advantage
1. Research:
 Proof concepts against real use
cases and data
 Using a powerful cutting-edge
technology
1. IT providers:
 Showcase latest technology
 Test and improve products for
real use cases and workload
6
http://www.sdil.de
German government initiative for boosting
Big Data use in top level research for
four business areas

7. SDIL Partners
Core PartnersCore Partners
Research PartnersResearch Partners
Project PartnersProject Partners
AssociationsAssociations

8. SDIL’s Basic Principle
8

9. Data Protection and Privacy –
SDIL’s Top Priority
• Any data processing takes place in compliance with German
data protection rules and regulations.
• All data available at the KIT can be saved in highly secure
format and cannot be accessed by third parties without access
control. Leading-edge state-of-the-art security technology is used
here.
• Industry data sources are only accessible if such access was
expressly granted by the data provider in advance.
• Results from processing data from different data providers and
whose authorship cannot be clearly established are not saved
within the platform as a matter of principle.
9

10. SDIL Platform at SCC
10

11. Agenda
11
2
Smart Data Innovation Lab – Why & What
KIT SCC and its role in SDIL
1
3 IBM‘s contribution: Watson Foundation on POWER
4 First projects and experiences

12. Karlsruhe Institute of Technology (KIT)
One of the largest and most prestigious research and education
institutions in Germany
12

13. Steinbuch Centre for Computing
KIT – Facts and Figures
* Budget 2013
24 778 Students
9 491 Employees
355 Professors
6 035 Scientists
~3 200 PhD students
24 778 Students
9 491 Employees
355 Professors
6 035 Scientists
~3 200 PhD students
844M € Budget*
270M € Federal funds
216M € State funds
358M € 3rd
party funds
844M € Budget*
270M € Federal funds
216M € State funds
358M € 3rd
party funds
129 Invention disclosures
52 Patent applications
25 Spin-offs
2.2M € Income from KIT
licenses
129 Invention disclosures
52 Patent applications
25 Spin-offs
2.2M € Income from KIT
licenses

14. Steinbuch Centre for Computing
Steinbuch Centre for Computing (SCC)
• Founded on January 1st, 2008
 Merger of the Computing Centers of former Karlsruhe University (URZ) and
Research Center Karlsruhe (IWR)
• Karl Steinbuch
 Professor at Karlsruhe University, creator of the term “Informatik”, co-
founder of the first German faculty of informatics
• Two locations at KIT Campus South and North
• 189 people in total (as of 1.9.2015)
 60% scientists, 40% technicians, administrative personnel, trainees
 7 departments and 4 research groups
• Board of directors
 Prof. Dr. Hannes Hartenstein
 Prof. Dr. Bernhard Neumair
 Prof. Dr. Achim Streit

15. Steinbuch Centre for Computing
Who are we?
What do we do?
Which demands do we satisfy?
“Services for Science – Science for Services”
Institute in KIT with
service tasks
 Computational Science &
Engineering (CSE)
 Data-Intensive Science (DIS)
 For users in KIT, BaWü,
Germany and international
 Research, education and innovation
in Supercomputing, Big Data and
secure IT-federations
 Operation of large scale research
facilities
 Operation of basic IT services

16. Enabling Data-Intensive Science (DIS)
• Operation of GridKa
 German Tier-1 in WLCG for an
international community
• Operation of the Large-Scale Data Facility
 Multi-disciplinary data centre for climate research,
systems biology, energy research, etc. in BaWü
• Joint R&D&I with scientific communities
 Generic data management research
 Data Life Cycle Labs in Helmholtz Programm SBD
• Innovation driver for SMEs,
big industry und start-ups
• Active role in national and international projects & initiatives

17. Agenda
17
2
Smart Data Innovation Lab – Why & What
KIT SCC and its role in SDIL
1
3 IBM‘s contribution: Watson Foundation on POWER
4 First projects and experiences

18. IBM Watson
Foundations
Software
Enterprise-
grade Big Data
Model-based
Predictive
Analytics
Semantic Text
Analysis
Cognitive
Computing
18
IBM’s Watson Foudation POWER cluster
260 disks with
>300 TB space
7 nodes
140 cores
2.800 virtual
systems
40 GB/s network
switch
4 TB RAM

19. Core Watson Foundation Technology for SDIL
19
WATSON FOUNDATIONS
Sales Marketing Finance Operations HRRisk ITFraud
IBM Watson™ and Industry Solutions
SOLUTIONS
CONSULTING AND IMPLEMENTATION SERVICES
BIG DATA & ANALYTICS INFRASTRUCTURE
Decision
Management
Planning &
Forecasting
Discovery &
Exploration
Business Intelligence & Predictive Analytics
Content
Analytics
Information Integration & Governance
Data Mgmt &
Warehouse
Hadoop
System
Stream
Computing
Content
Management
WATSON FOUNDATIONS
Sales Marketing Finance Operations HRRisk ITFraud
IBM Watson™ and Industry Solutions
SOLUTIONS
CONSULTING AND IMPLEMENTATION SERVICES
BIG DATA & ANALYTICS INFRASTRUCTURE
Decision
Management
Planning &
Forecasting
Discovery &
Exploration
Business Intelligence & Predictive AnalyticsBusiness Intelligence & Predictive Analytics
Content
Analytics
Information Integration & Governance
Data Mgmt &
Warehouse
Hadoop
System
Stream
Computing
Content
Management

20. Watson Cluster Architecture Overview
20

21. Watson Foundation Bootcamp in January 2015:
84 participants trained in SPSS and BigInsights in 2 days
21

22. Agenda
22
2
Smart Data Innovation Lab – Why & What
KIT SCC and its role in SDIL
1
3 IBM‘s contribution: Watson Foundation on POWER
4 First projects and experiences

23. SDIL Projects Overview
23
Industrie 4.0Industrie 4.0
EnergyEnergy
MedicineMedicine
Smart CitiesSmart Cities
• Condition-Based Maintenance (done)
• Industrial Log Analysis (running)
• SmartFactoryKL
Predictive
Maintenance (running)
• User Sensitive HMI (planned)
• Optimal Nesting (planned)
• Data Mining for Welding (planned)
• Paintshop Machine Learning (planned)
• Smart Brain Data Analytics (running)
• Machine learning for age-related
macular degeneration treatment
(running)
• Spinal cord injury analytics
(approved)
• Decentralized Energy Markets
Demonstrator (done)
• Predictive Analytics for Energy
Management (planned)
• Disaster Management
Demonsrator (done)
• Smart Grid (planned)

24. Smart Brain Analytics: Use Case
24
1. Human Brain Project (HBP)
 A human brain frozen at -80o
C
 Cut into 70μm thin slides
 Take image of the brain after
each extracted slide
 Segment the sectional planes
to build 3D model of the brain
 Use data analysis to replace
manual segmentation
 843 Brain slides
 1350×1950 pixels each image
 6.6 GByte RGB images
 42 MByte mask images
 Up to 2PB with extremely high
resolution image scanners

25. Project Brain: Methodology & Realization
25

26. Project Brain: Training & Testing Strategy
26

27. Project Brain: Preliminary Results
Manually marked
brain slice
SPSS-determined
brain slice
(98.8% accuracy)
27
Source
SPSS-model with
feature extraction
(99.53% accuracy)

28. Industrial Log File Analysis
Association Analysis for Data-Driven Services Based on Industrial Logs
•Challenge: existing solutions for analyzing industrial log files recordings
(e.g. alarms, machine logs, error messages, user interactions) are
restricted:
 They focus on isolated problem analysis and optimization
 They are not able to cover complex functions like revealing of hidden
correlations respectively prediction of events
 Work on relatively small data sets without parallelization and scalability
•Vision: Using the potential of a holistic analysis of industrial log files with
the following goals:
 Derive and evaluate appropriate analytical methods
 Choose parallelization and scalable strategies for data pruning and
features extraction
 Explore real-time and deployment options
28

29. Roles Profile Sensitive HMI
• Analyze user-machine-interaction to predict and provide an
optimized HMI assistance
29
Challenge:
 Anonymous and unknown users
 Billions of interaction options depended from production orders
 Production orders normally never will repeated
Vision:
 (Self-) Optimized user-machine-interface for every machine operator
 Increase productivity: avoid problems caused by operating issues

30. Top 10 Best Practices & Lessons Learned
10. One common demand: faster route from research to field
9. Consider the pipeline from internal data sources to SDIL, e.g. data
cleansing and pseudonymization
8. Sensitive person-related data is not the only reason for restrictive access
rules
7. Data privacy & confidentiality – not a technical, but a bureaucratic
challenge
6. Opportunity to rehearse processes for external data use in the cloud
5. Objective “Yes, but we have to do something…” is not appropriate
4. Accuracy is relative: sometimes 60% is great, but 99,2% - not enough
3. Algorithms on real data do not perform the same as on probes
2. Fruitful cooperation between business, IT and research experts
1. Information, not data, is the gold of 21. century, but… all that glitters is not
gold
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