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Big Data

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Lecture on Big Data

Guest Speaker Simon Trang Research Member at DFG RTG 1703 and Chair of Information Management Göttingen University, Germany 2014

The City
City of Göttingen

• Founded in the Middle Ages • True geographical center of Germany • 130,000 residents
Chair of Information Management Lecture on Big Data at Macquarie University 2 2

The University
Georg-August-Universität Göttingen (founded in 1737)

• • • •

One of nine Excellence Universities in Germany 13 faculties, 180 institutes 26,300 students (2013) 11.6% students from abroad (new entrants: approximately 20%) • 13,000 employees (including hospital and medical school), including 420 professors • 115 programs of study from A as in Agricultural Science to Z as in Zoology are offered (73 bachelor / 22 master programs)

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“The Göttingen Nobel Prize Wonder”
Over 40 Nobel prize winners have lived, studied, and/or lived, studied or/and researched 41 Prize researched at the University of Göttingen, among them… at the University of Göttingen, among them… • • • • • • • • • • • • • • Max von Laue, Physics, 1914 Max von Laue, physics, 1914 Max Planck, physics, 1918 Max Planck, Physics, 1918 Werner Heisenberg, physics, 1932 Werner Heisenberg, Physics, 1932 Otto Hahn, chemistry 1944 Otto Hahn, Chemistry 1944 Max Born, physics, 1954 Max Born, Physics, 1954 Manfred Eigen, chemistry, 1967 Manfred Eigen, Chemistry, 1967 Erwin Neher, medicine, 1991 Erwin Neher, Medicine, 1991

Max Planck

Werner Heisenberg

• Stefan Hell, chemistry, 2014 (together with Eric Betzig and William E. Moerner)
Manfred Eigen

Erwin Neher

Chair of Information Management

Lutz M. Kolbe

4

Famous Graduates from Göttingen
• Otto von Bismarck • Dieter Bohlen
First German chancellor, state examination in law German pop musician, M.Sc. in Administration CEO Allianz AG, law and philosophy Mathematician and director of the observatory, professor of astronomy President of SIEMENS AG, M.Sc. in Administration U.S.-American businessman, founder of J.P. Morgan & Co. Former German chancellor, state examination in law Head of German parliament, state examination in teaching, honorary professor at the University of Göttingen German federal president, state examination in law and history
J.P. Morgan Gerhard Schröder

• Michael Diekmann

• Carl Friedrich Gauß • Klaus Kleinfeld

• John Pierpont Morgan
• Gerhard Schröder • Rita Süßmuth

• Richard von Weizsäcker • Konrad Zuse

Inventor of the first binary computer, honorary professor at the University of Göttingen

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Research Projects

1 Mobility (SMRG)
• • • • BESIC e-Radschnellwege Elektromobilität für Nachahmer Gap EV

Renewable Resources
Research Training Group 1703 “Resource Efficiency in Corporate Networks”

2

3 IT Departments
Chair of Information Management

Efficiency Evaluation of Service Configurations in Telemedicine

Consumerization of IT in Financial Services and Automotive

Digital Transformation
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6

Why Study in Göttingen?

If you want to come to Göttingen, please contact me: strang@uni-goettingen.de There are indeed a lot of very good reasons, for example:
Hamburg

Berlin
Göttingen

Munich
Chair of Information Management Lutz M. Kolbe 7

Agenda

Why Big Data?

Where does the big data come from?

What are the technologies?

Big data, a friend or foe?

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Lecture on Big Data at Macquarie University

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Hype or Ripe?

Chair of Information Management

From Peta- to Zettabytes

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Examples of really big data sets

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Big data is particularly challenging: The three Vs
100 km, 5 Exabyte

2.5 Exabyte of new data each day!
=2,500,000,000,000,000,000 bytes

50 km

50 km 2.5 Exabyte

1 Terrabyte 2.5 Exabyte 2014
Chair of Information Management

2016

So what is Big Data?
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Constant improvements in traditional DBMS technology as well as new databases like NoSQL increase the ability to handle larger amounts of data. With this difficulty, new platforms of big data tools are being developed to handle various aspects of large quantities of data. Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead massively parallel software running on tens, hundreds, or even thousands of servers.

Chair of Information Management

New big data analytics applications impacting core business processes can be found in all industry and service sectors

Chair of Information Management

Agenda

Why Big Data?

Where does the big data come from?

What are the technologies?

Big data, a friend or foe?

Chair of Information Management

Lecture on Big Data at Macquarie University

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Where does all the data come from?

Pope Benedict Inauguration (2005)

Pope Francis Inauguration (2013)

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Mobile service providers sell user locations for traffic jam prediction services

Low density

High density

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Without regulation, companies save as much as they can Facebook, I would like to see all data you have stored about me

• After some time passed, Facebook sent him 1200 DIN A4 pages • Data that had been deleted was still in the document • His request resulted in rethinking the data privacy policies at the European level
Chair of Information Management Lecture on Big Data at Macquarie University 18

How much is your personal data worth?
Political interest: 2 AUD

Marriage: 11.5 AUD

Residence: 7 AUD

Illnesses: 30 AUD

New job: 8 AUD

Reduce weight: 11 AUD
Pregnancy: 11 AUD

House moving: 8.5 AUD

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What is a fair amount to be paid for personal data?
• Controversial study of Staiano and colleagues supported by Telefonica and Telecom Italia • What is the lowest price you are willing to accept for your data? • Daily valuation of PII of 60 participants in a second bet reverse auction • The median bid across all the data categories was 2 EUR (2.8 AUD) a day -> 730 EUR (1022 AUD) a year for one category

Source: Staiano et al. (2014): Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data, in Ubicomp 2014. Chair of Information Management Lecture on Big Data at Macquarie University 20

Why do we share our data with profit organizations (almost) for free?
• A new loyalty card is introduced to the customers • You would get an initial discount if you join now • A game theory approach:

No
Would you join?

Others (would they join) Yes good very bad Nash bad bad Equilibrium
21

No

Pareto ok ok Efficient

Yes
Chair of Information Management

very bad good Lecture on Big Data at Macquarie University

Agenda

Why Big Data?

Where does the big data come from?

What are the technologies?

Big data, a friend or foe?

Chair of Information Management

Lecture on Big Data at Macquarie University

22

Traditional business intelligence vs. next-generation business intelligence
Business Intelligence (BI) Next-Generation BI

• Focus on descriptive analytics • E.g., how many Wiener sausages did we recently sell to young consumers in Australia?

• Focus on predictive and prescriptive analytics • E.g., which Australian groups should be addressed in order to significantly increase our Wiener sausages sales and profit in 2015?
23

Chair of Information Management

Lecture on Big Data at Macquarie University

Techniques for Big Data analytics
• • • • • • • • • • • • • • • Hierarchical clustering Centroid-based clustering Distribution-based clustering Density-based clustering … and many more Linear regression model Discrete choice models Logistic regression Probit regression … and many more Neural networks Multilayer Perceptron (MLP) Radial basis functions Support vector machines … and many more
Lecture on Big Data at Macquarie University 24

Cluster analysis techniques

Regression analysis techniques

Machine learning techniques

Chair of Information Management

An example of a cluster analysis



Three different clusters of organizations emerged from the data Internal operations Market competitiveness Reputation management

• • •

Source: Schmidt et al. (2010): Examining the Contribution of Green IT to the Objectives of IT Departments: Empirical Evidence from German Enterprises, in Australasian Journal of Information Systems 17(2014)1. Chair of Information Management Lecture on Big Data at Macquarie University 25

Between 2002 & 2013, Target increased its revenue by 29 billion USD: An example of a probit estimation (1)

Only for you
Discount on baby clothes and cribs

“My daughter got this in the mail!” “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

A few days later target called back

“I had a talk with my daughter,” “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”
Lecture on Big Data at Macquarie University 26

Chair of Information Management

An example of a probit estimation (2)
• Target wanted to advertise to pregnant woman • What buying behavior can predict pregnancy?

1

Product1

2 Data sets of prior purchases
Pregnancy

Product2
Product3 Etc.

3

Product1 Product2

.30*** .01ns .21***
Probability Pregnancy

4 Data sets of new purchases
If prob. preg. > .90
27

Product3
Etc.

Chair of Information Management

Lecture on Big Data at Macquarie University

Relation of the probability of a credit default and computer fonts: An example of a Neural Network (1)

Online credit
Only if

Compute probability of default
Chair of Information Management Lecture on Big Data at Macquarie University 28

Relation of the probability of a credit default and computer fonts: An example of a Neural Network (2)

If prediction is correct -> do nothing
… either If prediction is wrong -> change weights

Iterative training of the network (with 2/3 of data)

Count correct predictions

Count wrong predictions

Success rate

Validating the network (with 1/3 of data)
Chair of Information Management Lecture on Big Data at Macquarie University 29

Agenda

Why Big Data?

Where does the big data come from?

What are the technologies?

Big data, a friend or foe?

Chair of Information Management

Lecture on Big Data at Macquarie University

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Big Data transfers power to companies
• Controversial study of Kramer and colleagues published in PNAS 111 (2014) 24 • Can emotions be transferred to others without direct interaction? • Experiment with N = 689,003 Facebook users, maybe you?!
Manipulation through Facebook Timeline displays many positive expressions Timeline displays fewer positive expressions User reaction Positive emotions (more positive posts) Negative emotions (more negative posts)
Lecture on Big Data at Macquarie University 31

Chair of Information Management

Group 2

Group 1

Big data and related threats
• Algorithms only show correlations rather than explaining the deeper causes
Amount of storks Chocolate consumption

+ +

Rural area ??? Developed ??? country

+ +

Number of pregnancies Nobel Prizes

• Data gives power to organizations and public authorities

1

2

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Thank you for your attention

“We know where you are… We know where you've been… We can more or less know what you’re thinking about”

Eric Schmidt, CEO at Google
Eric Schmidt, CEO Google, at "Second Annual Washington Ideas Forum" on 1st Oktober 2010

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