Big Data Technologies & Applications

Restricted © Siemens AG 2014. All rights reserved

Big Data Technologies &

Applications

EU BYTE 1st Workshop - Lyon, 11 September 2014

Sebnem Rusitschka Siemens AG

Restricted © Siemens AG 2014. All rights reserved

Big Data Technologies & Applications

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/

Overview

2014-09-11 Sebnem Rusitschka Siemens AG

http://byte-project.eu/

Unrestricted © Siemens AG 2014. All rights reserved

Innovations in distributed storage and computing

enable cost-effective handling of the 3 Vs

A short history of Big Data Technologies

3 2014-09-11 Sebnem Rusitschka Siemens AG


2013 brought about a common understanding that

technologies are there to query all your data

The “Lambda Architecture” introduced by Nathan Marz

2014-09-11 Sebnem Rusitschka Siemens AG4


Cost-effective handling of analytics will foster

advancing analytical capabilities of businesses

Value and Complexity

Inform

Analyze

Act

Descriptive

Examples

• Plant operation

report

• Fault report

Current penetration across all industries (according to Gartner 2013)

Adopt d

by vast

majority

99%

What happened?

Diagnostic

• Alarm management

• Root cause

identification

Adopted

by

minorities

30%

Why did it happen?

Predictive

• Power consumption

prediction

• Fault prediction

Still few

adopters13%

What will happen?

Prescriptive

• Operation point

optimization

• Load balancing

Very few

early

adopters

3%

What shall we do?



Industry Applications Example:

Real-time prescriptive analytics for gas turbines

• 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

Benefits

Streaming Data: ca. 5,000 variables / s

Complete Data and Dependency Analysis

plus Learning Optimization

Input data and model results

Mo

du

les

Real-time Data Analysis (1,000 Neural Models)

Source: Siemens AG



There is a trade-off between enhancing interpretability of

data and preserving privacy & confidentiality

Emerging Big Data Needs and Trends (1/2)


Semantic heterogeneity due to variety of data/description owners: Over 60 % of all Linked Open

Data use proprietary vocabulary 1)

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 Data Interpretability

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

Increasing Importance of Security, Legal, Social Aspects

1) V. Christophides, “Web Data Management: A Short Introduction to Data Science”, Lecture Notes, Spring 2013, p. 15,

http://www.csd.uoc.gr/~hy561/Lectures13/CS561Intro13.pdf

2) de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel (March 25, 2013). "Unique in the Crowd: The privacy

bounds of human mobility". Nature srep. doi:10.1038/srep01376.

http://www.csd.uoc.gr/~hy561/Lectures13/CS561Intro13.pdf


Analytics needs to better blend with available and

emerging big data computing

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)

Challenge Need

The Evolution from Query Engine to Analytics Engine

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

Abstraction from underlying

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

http://www.paradigm4.com/wp-content/uploads/2014/06/P4PR07012014.pdf


Looking forward to questions & feedback!

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:

[email protected]

siemens.com/innovation

mailto:[email protected]

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