Author: Sebnem Rusitschka, Siemens AG Presented at: BYTE 1st Workshop - Lyon, 11 September 2014

1. EU BYTE 1st Workshop - Lyon, 11 September 2014
Big Data Technologies &
Applications
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Sebnem Rusitschka Siemens AG

2. Restricted © Siemens AG 2014. All rights reserved
Big Data Technologies & Applications
Overview
 The Evolution of Big Data Technologies
 Analytics & Big Data Applications
 Emerging Big Data Needs & Trends
 Key Take Aways in Panel Discussion
 Detailed analyses see http://byte-project.eu/
2014-09-11 Sebnem Rusitschka Siemens AG

3. Innovations in distributed storage and computing
enable cost-effective handling of the 3 Vs
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A short history of Big Data Technologies
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4. 2013 brought about a common understanding that
technologies are there to query all your data
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The “Lambda Architecture” introduced by Nathan Marz
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5. Cost-effective handling of analytics will foster
advancing analytical capabilities of businesses
What will happen?
What shall we do?
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Value and Complexity
Inform
Analyze
Act
Descriptive
What happened?
Examples
• Plant operation
report
• Fault report
Why did it happen?
Current penetration across all industries (according to Gartner 2013)
Adopt d
by vast
majority
99%
Diagnostic
• Alarm management
• Root cause
identification
Adopted
by
minorities
30%
Predictive
• Power consumption
prediction
• Fault prediction
Still few
adopters 13%
Prescriptive
• Operation point
optimization
• Load balancing
Very few
early
adopters
3%
2014-09-11 Sebnem Rusitschka Siemens AG

6. Industry Applications Example:
Real-time prescriptive analytics for gas turbines
Benefits
• Improved turbine
ramp-up with less
vibrations (lower
maintenance needs)
• Reduced NOx
Emissions
• Increase of turbine
efficiency in
operations
• Guiding turbine
development
process in planning
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Streaming Data: ca. 5,000 variables / s
Input data and model results
Complete Data and Dependency Analysis
plus Learning Optimization
Modules
Real-time Data Analysis (1,000 Neural Models)
Source: Siemens AG
2014-09-11 Sebnem Rusitschka Siemens AG

7. There is a trade-off between enhancing interpretability of
data and preserving privacy & confidentiality
Increasing Importance of Data Interpretability
 Semantic heterogeneity due to variety of data/description owners: Over 60 % of all Linked Open
 EU Optique: aims at giving end users scalable semantic access to Big Data, e.g. by inferring
and (semi-) automating semantic linkage of data, correlations, and knowledge.
Increasing Importance of Security, Legal, Social Aspects
 Big Data Analytics circumvents anonymization: 4 spatio-temporal points, approximate places
and times, are enough to uniquely identify 95% of 1.5M people in a mobility database with
metadata 2)
 EU BYTE: taking European Big Data technology roadmaps to the next level by focusing on
maximizing positive and diminishing negative externalities, by analyzing sustainable business
models
1) V. Christophides, “Web Data Management: A Short Introduction to Data Science”, Lecture Notes, Spring 2013, p. 15,
2) de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel (March 25, 2013). "Unique in the Crowd: The privacy
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Emerging Big Data Needs and Trends (1/2)
Data use proprietary vocabulary 1)
http://www.csd.uoc.gr/~hy561/Lectures13/CS561Intro13.pdf
bounds of human mobility". Nature srep. doi:10.1038/srep01376.
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8. Analytics needs to better blend with available and
emerging big data computing
Challenge Need
 Analytics becomes part of each step of the data refinery pipeline, e.g. by
 detecting and remedying data quality issues at acquisition time
 analyzing effective use and untapped potentials in data usage
 big data storage & computing to enable ease of use for data scientists
 analytics workflows & management to enable ease of use for business users
1) Paradigm 4, “Leaving Data on the Table”, Survey, 1 July 2014. http://www.paradigm4.com/wp-content/uploads/2014/06/P4PR07012014.pdf
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Emerging Big Data Needs and Trends (2/2)
Although 49 % of the data scientist could not fit
their data into relational databases anymore:
only 48 % have had used Hadoop or Spark
76 % of those could not work effectively 1)
The Evolution from Query Engine to Analytics Engine
 Abstraction from underlying
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9. Looking forward to questions & feedback!
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Contact
Sebnem Rusitschka
Senior Key Expert
Prescriptive Analytics & In-field Applications
Siemens AG
Corporate Technology
Business Analytics & Monitoring
Otto-Hahn-Ring 6
D-81379 Munich
Phone: +49 (89) 636-44127
Fax: +49 (89) 636-41423
Mobile: +49 (172) 357 59 35
E-mail:
sebnem.rusitschka@siemens.com
siemens.com/innovation

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