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From Big to Smart Data - Smart Data Innovation Lab Overview
1. Š 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
2. ⢠IBMâs statements regarding its plans, directions, and intent are subject to change or withdrawal
without notice at IBMâs sole discretion.
⢠Information regarding potential future products is intended to outline our general product direction
and it should not be relied on in making a purchasing decision.
⢠The information mentioned regarding potential future products is not a commitment, promise,
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⢠The development, release, and timing of any future features or functionality described for our
products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a
controlled environment. The actual throughput or performance that any user will experience will vary
depending upon many factors, including considerations such as the amount of multiprogramming in
the
userâs job stream, the I/O configuration, the storage configuration, and the workload processed.
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 Technologyâs 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 â 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 â Why & What
KIT SCC and its role in SDIL
1
3 IBMâs 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
ď§ With new algorithms and methods
ď§ On the basis of securely handled data
ď§ In the framework of well-defined projects
Supported by
6. SDIL: WIN-WIN-WIN between
1. Industry:
ď§ Lower threshold to experiment
with Big Data analytics
ď§ Access to cutting-edge
research and technology
ď§ Leverage Smart Data for
tangible business advantage
1. Research:
ď§ Proof concepts against real use
cases and data
ď§ Using a powerful cutting-edge
technology
1. IT providers:
ď§ Showcase latest technology
ď§ 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
9. Data Protection and Privacy â
SDILâs 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
11. Agenda
11
2
Smart Data Innovation Lab â Why & What
KIT SCC and its role in SDIL
1
3 IBMâs 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 â 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
ď§ Merger of the Computing Centers of former Karlsruhe University (URZ) and
Research Center Karlsruhe (IWR)
⢠Karl Steinbuch
ď§ Professor at Karlsruhe University, creator of the term âInformatikâ, 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)
ď§ 60% scientists, 40% technicians, administrative personnel, trainees
ď§ 7 departments and 4 research groups
⢠Board of directors
ď§ Prof. Dr. Hannes Hartenstein
ď§ Prof. Dr. Bernhard Neumair
ď§ Prof. Dr. Achim Streit
15. Steinbuch Centre for Computing
Who are we?
What do we do?
Which demands do we satisfy?
âServices for Science â Science for Servicesâ
Institute in KIT with
service tasks
ď§ Computational Science &
Engineering (CSE)
ď§ Data-Intensive Science (DIS)
ď§ For users in KIT, BaWĂź,
Germany and international
ď§ Research, education and innovation
in Supercomputing, Big Data and
secure IT-federations
ď§ Operation of large scale research
facilities
ď§ Operation of basic IT services
16. Enabling Data-Intensive Science (DIS)
⢠Operation of GridKa
ď§ German Tier-1 in WLCG for an
international community
⢠Operation of the Large-Scale Data Facility
ď§ Multi-disciplinary data centre for climate research,
systems biology, energy research, etc. in BaWĂź
⢠Joint R&D&I with scientific communities
ď§ Generic data management research
ď§ 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 â Why & What
KIT SCC and its role in SDIL
1
3 IBMâs 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
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IBMâs 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
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 â Why & What
KIT SCC and its role in SDIL
1
3 IBMâs contribution: Watson Foundation on POWER
4 First projects and experiences
24. Smart Brain Analytics: Use Case
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1. Human Brain Project (HBP)
ď§ A human brain frozen at -80o
C
ď§ Cut into 70Îźm thin slides
ď§ Take image of the brain after
each extracted slide
ď§ Segment the sectional planes
to build 3D model of the brain
ď§ Use data analysis to replace
manual segmentation
ď§ 843 Brain slides
ď§ 1350Ă1950 pixels each image
ď§ 6.6 GByte RGB images
ď§ 42 MByte mask images
ď§ Up to 2PB with extremely high
resolution image scanners
28. Industrial Log File Analysis
Association Analysis for Data-Driven Services Based on Industrial Logs
â˘Challenge: existing solutions for analyzing industrial log files recordings
(e.g. alarms, machine logs, error messages, user interactions) are
restricted:
ď§ They focus on isolated problem analysis and optimization
ď§ They are not able to cover complex functions like revealing of hidden
correlations respectively prediction of events
ď§ Work on relatively small data sets without parallelization and scalability
â˘Vision: Using the potential of a holistic analysis of industrial log files with
the following goals:
ď§ Derive and evaluate appropriate analytical methods
ď§ Choose parallelization and scalable strategies for data pruning and
features extraction
ď§ 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:
ď§ Anonymous and unknown users
ď§ Billions of interaction options depended from production orders
ď§ Production orders normally never will repeated
Vision:
ď§ (Self-) Optimized user-machine-interface for every machine operator
ď§ 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 â not a technical, but a bureaucratic
challenge
6. Opportunity to rehearse processes for external data use in the cloud
5. Objective âYes, but we have to do somethingâŚâ 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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