smart data fo the smart cities and smart factories in the future

Dr. Rene Fassbender CEO & Founder

[email protected] www.omegalambdatec.com

19. November 2015

Year of the Goat – Frankfurt

Smart Data for the Smart Cities and Smart Factories of the Future

Agenda

!   Smart Data in Astrophysics – learning from the Universe

!   Smart Data Analyses for Smart Cities – Ideas for Future Projects

!   Smart Factories – moving forward towards Industry 4.0

!   Disaggregation of Smart Sensor Data – new Services from Smart

Analyses

!   Brain Gymnastics & Smart Data Workshop

<2> Dr. Rene Fassbender


I. Smart Data in Astrophysics –

learning from the Universe

Golden Age of Astrophysics

The digital revolution in Astrophysics happened 15 years ago


Public digital astronomical data volume in the online archive of the European Southern Observatory (ESO) in Terabyte

Image Source: ESO

Digital Treasure Hunting in Astrophysics I


Question: Where are the scientific treasures in these public data?

1/200,000 of the sky in X-rays (XMM-Newton)

1/200,000 of the sky in the optical (Subaru Telescope)

Scientific References: Fassbender 2007; Fassbender et al. 2011; Fassbender et al. 2014


Answer: Where they can just barely be found using the best combined smart analyses!

Digital Treasure Hunting in Astrophysics II

Scientific References: Fassbender 2007; Fassbender et al. 2011; Fassbender et al. 2014

1/200,000 of the sky in X-rays (XMM-Newton)

1/200,000 of the sky in the optical (Subaru Telescope)

The identified scientific digital treasures of yesterday define the reasearch frontier of today


Scientific References: Fassbender et al. 2014; Tozzi et al. 2015; Santos et al. 2015; Hayden, Perlmutter et al. 2015

Current Research Topics: !   How does structure and galaxy formation work in detail in the early Universe? !   How does the Dark Energy component (OmegaLambda) evolve with cosmic time?

combined optical+infrared+X-ray composite

infrared image observed with the Hubble Space Telescope in August 2015

Scale of Image: 3 Million Light Years

Lookback Time: 9.5 Billion Years

Quantities we can nowadays accurately measure at a distance of 10 billion light years (1026 m)

Physical Quantity Used Data

total mass of the galaxy cluster X-rays, sub-mm, optical/NIR, HST

gas temperature of the intracluster medium X-ray spectroscopy

gas mass & spatial gas distribution X-ray imaging

distribution of Dark Matter X-rays, optical/NIR, HST

spatial distribution of galaxies & stellar light optical/NIR/MIR, HST

age & total mass of the stars in galaxies UV/optical/NIR/MIR, spectroscopy

distance to the galaxy cluster optical spectroscopy

discovery of new distant objects X-rays, sub-mm, NIR/MIR

galaxy type determination optical/NIR/MIR, HST, spectroscopy

radial velocities of galaxies in cluster optical spectroscopy

detailed structure and morphology of galaxies Hubble Space Telescope

spatially resolved local star formation rate far infrared, UV, 3D-spectroscopy

merger dynamics of galaxies NIR 3D-spectroscopy

activity of galactic Black Holes (AGN) X-rays, HST, 3D-spectroscopy


Cosmic clusters of galaxies are the giant relatives of metropolitan areas


Data and structures are self-similar, hence the Smart Data methods of astrophysics are directly applicable to Smart Cities etc.

x1018 in scale

Galaxy Cluster Abell 1689 Berlin at Night

Cosmic Cities vs Smart Cities


Available Data: !   optical & UV imaging

!   near-infrared imaging

!   mid-infrared imaging

!   far-infrared data

!   high-resolution HST imaging

!   optical spectroscopy

!   NIR 3D-spectroscopy

!   high-resolution X-ray data

!   spectroscopic X-ray data

!   radio observations

!   sub-millimeter data

Available (real-time) data: !   weather

!   traffic

!   construction sites

!   calendar of event

!   Smart Sensor data

!   digital city-citizen-interaction

!   current location data

!   social media data

!   city quarter statistics

!   and many more

!   ...

Smart Data Analyses


II. Smart Data Analyses for Smart Cities –

Ideas for Future Projects

How to turn cities into Smart Cities


sum of all available data +

Smart Data Analyses =

Smart Cities

Any form of public and easily accessible (machine readable) digital data provided by the cities will make them smarter in the future

Predictive-Rent-MUC: Future development of rental prices in Munich


Problem: In which city quarters will the rent for apartments increase the most over the next years?

Data: historic rental index, city quarter statistics, leading market indicators

Applications of rent pricing forecasts: !   optimized investment planning for investors, buyers, and tenants !   site optimizations for new restaurants, shops, and companies !   improved planning capabilities for cities, agencies, and social services

Image Source: OmegaLambdaTec

2012 2014 2015

Predictive-Taxi-MUC: Taxi demand forecasts for Munich


Problem: How many taxis are needed in the next 15min at a given location in Munich?

Data: event calendar, weather, traffic updates, site information, calibration data

Applications of a taxi demand forecast model: !   predictive demand management: taxis, car sharing, bike taxis !   shortened waiting times & improved service for customers !   savings in gas, time, and costs for local mobility providers !   optimized predictive planning of personnel for local mobility providers

Image Source: Goolge Maps/OmegaLambdaTec

expected taxi demand within the next 15 minutes

high demand

medium demand

moderate demand

360°-Mobility-MUC: Real-time mobility zones for Munich


Problem: Within what zone is a person currently mobile within the next 15 minutes given the available means of transport and the current location?

Data: real-time traffic & public transport, weather, construction sites, geo location data

Applications of real-time-360°-mobility-zones: !   optimized zone coverage: emergency services, fire departments, police, taxi services !   meeting point optimizations: friends, dates, business meetings, handover of material !   real-time-planning: delivery services, transport services !   site optimizations: delivery services, transport services, service stations

15 minute-real-time- mobility-zones for three persons with different means of

transport

Image Source: Goolge Maps/OmegaLambdaTec

Sky-Live-MUC: Real-time identification of all bright light sources in the Munich night sky


Problem: What is the origin and underlying source of this light in the Munich night sky?

Data: real-time video data, air traffic data, astronomy data, weather

Applications of a real-time night sky analysis: !   education & knowledge: identification of satellites, air planes, planets, stars, meteors !   information & security: automated identification of falling stars & unidentified light sources !   real-time weather: current cloud coverage & atmospheric transparency !   night sky statistics: number of air planes, meteors, clouds etc. Image Source: OmegaLambdaTec

night sky over Munich on

15. March 2015 at 20:55:24

European Lighthouse Smart City Munich

!   Munich has just won a European Horizon-2020 Lighthouse Smart City Project in partnership with Vienna and Lyon

!   many Smart City projects and concepts will be tested in designated city quarters starting in 2016

!   one such project is Smart Lighting, for which street lamp posts are equipped with new sensors and services, e.g. •  as infrastructure hubs (WLAN, charging stations, etc.) •  as emergency and first aid stations (call buttons, equipment, etc.) •  for sensor-based Smart Services (Smart Parking, automatic light adjustment,

warnings, etc.) •  for Smart Data applications (traffic monitoring & control, micro weather

monitoring, Smart Traffic Lights, etc. ) •  and much more



III. Smart Factories –

moving forward towards Industry 4.0

The Enterprise Data Challenge

Currently 80% of all generated and collected data in German companies are not systematically analyzed

Thus a huge data potential is not fully exploited, e.g. for:

1.  the development of new digital business models

2.  data driven process and production optimizations

3.  new insights and information advantages over competitors


Industry 2015 vs Industry 4.0


Typical Industry 2015 Setting Industry 4.0 Setting company data, production data, and sensor data are stored locally where they are generated

all generated data within a company is stored in a central repository, from where everything can easily be accessed

data is taken until the hard drive is full, then old data is overwritten

all digital historic data records are archived to allow data mining in the future

an actual systematic analysis and exploitation of the data is happening on the few percent level

100% of the data is used and exploited, e.g. for monitoring, characterizing, and forecasting

the most widely used analysis tool of choice is Excel

automatic analysis pipelines based on data-centered programing languages master the data processing, complemented by new Big Data and visualization tools

production data is looked at after problems occurred

all production data is automatically monitored in real-time and data-driven warnings are generated before production problems occur

machine data is looked at after machines break down

permanent automatic monitoring of all machine data allows predictive maintenance before things break down

Smart ETA Factory 4.0 The energy efficient model factory of the future


Image Source: http://www.eta-fabrik.tu-darmstadt.de/eta/index.en.jsp

Aim optimization of the factory taking into account all sub-systems Site Technical University Darmstadt Completion of the Factory March 2016

computer model and construction status


IV. Disaggregation of Smart Sensor Data –

new Services from Smart Analyses

Automated Energy Disaggregation (AED)


Problem: Can the aggregated total power consumption signal be disaggregated into individual devices?

Data: current total power consumption as measured by intelligent meters

Applications of a disaggregation solution: !   Real-Time-Monitoring: When are which devices turned on? !   Controlling: What is the monthly electricity cost associated with each device? !   Cost Optimization: How can the total electricity bill be minimized? !   Green-Education / Energy Saving Awareness

Image Source: J.Z. Kolter & M.J. Johnson 2011

Data Base: Intelligent Meters target specifications: 1 Watt accuracy, 1 data point per second


time of day

total power [Watts]

Data Source: Apartment Tal 15, München

Calibration & Training Data as starting point for the development of a Smart Data solution


aggregated total power consumption

consumption of individual devices

Data Source: Apartment Tal 15, München

Development Aim: a fully automated, machine-learning real-time-energy-disaggregation-solution


Currently running: Oven (2200 Watts) (Real-Time-Monitoring) TV ( 550 Watts)

Electric Heater (2000 Watts) Light ( 320 Watts)

Usage Statistics Day & Week: (Controlling & Optimization)

Electricity bill last month: Oven (28.5 Hours / 15.50 Euros) (Controlling & Optimization) TV (72.2 Hours / 7.40 Euros)

Electric Heater (29.6 Hours / 14.30 Euros) Light (154 Hours / 17.40 Euros) Washing Machine (11.8 Hours / 3.40 Euros) Standby-Devices (720 Hours / 14.40 Euros)

Usage Stats Week

Smart Data Cooperation Model


Cities/Utility Companies/ Businesses

Citizens/Costumers

new Smart Data

Services

internal Data

public Data

Solution Development !   Smart Data Analyses !   Development of Solutions !   Maintenance and further

Development of the Smart Data Solutions

!   Smart Data Service Offers !   Internal Data Processing !   City/Customer-Interaction


V. Brain Gymnastics & Smart Data Workshop


Brain Gymnastics with the Goat

1 2 3

? ? ? One

cabbage, three doors, I take door

#1.


1 2 3

? ? Should I change

from door #1 to door

#2 ???



1 2 3 ? ?

Should I change


#2 ???

1.  No, the first choice is always the best choice! 2.  No, now my odds with door #1 are even higher!

3.  Now that the odds are 50/50, I just stay with #1! 4.  Yes, changing to door #2 will double my odds!

5.  Its random, the probability cannot be quantified!



1 2 3

? ? ?

⅓ ⅔



1 2 3

? ? !!!

⅓ ⅔ 0 More information: https://en.wikipedia.org/wiki/Monty_Hall_problem


Take Home Message I


The intuition of the majority is not always right.

Beware of spurious statistical results I


Source: http://tylervigen.com/spurious-correlations

Correlation: 99.79%


Source: http://tylervigen.com/spurious-correlations

Correlation: 99.26%

Beware of spurious statistical results II


Shit in, shit out!

Take Home Message II

Goat Cosmology


1.  The size of the Universe is larger than 13.7 Giga light years!

2.  The Universe expands at an accelerated rate!

3.  The Universe cannot be infinite in size and age, static and

homogeneous!

4.  The closest stars are several light years away!

5.  Nothing, of course the night sky is dark!

What conclusion about our Universe can be drawn based on the simple observation that the night sky is dark?

Night Sky

Goat Cosmology


1.  The size of the Universe is larger than 13.7 Giga light years!

2.  The Universe expands at an accelerated rate! 3.  The Universe cannot be infinite in size and age, static

and homogeneous!

4.  The closest stars are several light years away!

5.  Nothing, of course the night sky is dark!

What conclusion about our Universe can be drawn based on the simple observation that the night sky is dark?

Night Sky

Olbers‘ Paradox and the Dark Night Sky


In a static, eternal and infinite Universe with an infinite number of stars the night sky would be as bright as the surface of the sun

More information: https://en.wikipedia.org/wiki/Olbers'_paradox Image Sources: https://www.e-education.psu.edu/astro801/content/l10_p2.html http://www.universeadventure.org/big_bang/images/conseq-infinitestars.jpg

Line-of-Sight Argument Light-in-Shells Argument

The amount of star light reaching us from concentric shells is on average constant (distance effect: × r-2, number of stars: × r2), i.e. the sum of an infinite number of shells is infinite

Any line-of-sight will at some point end on the surface of a star, hence the apparent surface brightness in any direction is comparable to looking directly towards the sun

Take Home Message III


Profound conclusions may be based on very

simple observations.

Big Bang Cosmology all around us


Every cubic centimeter (cm3) contains 410 photons of the Cosmic Microwave Background (CMB) and 330 cosmic neutrinos as relics of the Big Bang.

In consequence, more than 1016 (10 Million Billion) cosmic neutrinos per second pass through our bodies at any time. What are the measurable effects of this constant bombardment by cosmic background neutrinos?

1.  The Big Bang has proven negative effects on my health!

2.  The neutrino signal can be easily measured with large radio antennas!

3.  Experiments in underground laboratories measure relic cosmic background neutrinos

routinely!

4.  Relic cosmic background neutrinos will not be directly detectable for the next 50-100

years!

5.  There is no cosmic neutrino background!

Big Bang Cosmology all around us


Every cubic centimeter (cm3) contains 410 photons of the Cosmic Microwave Background (CMB) and 330 cosmic neutrinos as relics of the Big Bang.

In consequence, more than 1016 (10 Million Billion) cosmic neutrinos per second pass through our bodies at any time. What are the measurable effects of this constant bombardment by cosmic background neutrinos?

1.  The Big Bang has proven negative effects on my health!

2.  The neutrino signal can be easily measured with large radio antennas!

3.  Experiments in underground laboratories measure relic cosmic background neutrinos

routinely! 4.  Relic cosmic background neutrinos will not be directly detectable for the next

50-100 years!

5.  There is no cosmic neutrino background!

More information: http://www.xray.mpe.mpg.de/theorie/cluster/SS10/02_SS10_Cosmology2_RF.pdf

Take Home Message IV


Big numbers and ubiquity do not necessarily

imply that things can be directly measured.

Standard Rulers in the Universe


What is the longest standard ruler (tape measure) with a well-defined and known dimension in the Universe?

1.  1m

2.  1 AU = 149.6 Million km

3.  8.30 kpc = 2.56x1020 m

4.  147.8 Mpc = 4.56x1024 m

5.  13.7 Giga light years = 1.30x1026 m

Standard Rulers in the Universe


What is the longest standard ruler (tape measure) with a well-defined and known dimension in the Universe?

1.  1m

2.  1 AU = 149.6 Million km

3.  8.30 kpc = 2.56x1020 m

4. 147.8 Mpc = 4.56x1024 m

5.  13.7 Giga light years = 1.30x1026 m

The Baryonic Acoustic Oscillation (BAO) Scale


References: Anderson et al. 2012; Veropalumbo et al. 2015

The BAO standard ruler scale is a theoretically well-understood imprint of the sound horizon scale at the epoch of recombination (380,000 years after the Big Bang) in the matter distribution of the Universe. It is nowadays routinely measured as a percent-level

overdensity peak in the spatial distribution of different object classes.

Take Home Message V


Some measurements that seem very hard to

impossible to do are nowadays routine business.

The value of customer data information


Your business has 10 million customers in Germany and has built up a unique data base with an unrivaled amount of information about them. Based on these data, what can be inferred about the overall population in Germany?

1.  Everything, I just need to multiply the measured numbers in my customer

data base by 8.1 to get the answer for Germany as a whole!

2.  Everything, I just need to carefully account for the statistical errors!

3.  It depends, on how well I understand my customers!

4.  Not much, since there is only data for 1/8 of the overall population!

5.  Nothing, since more than 70 Million people are missing in my data base!

Selection Effects and Sample Bias for an online XL-shoe-shop selling only sizes 42+


Image Source: http://www.die-welt-der-schuhe.de

percentage of overall population having a given shoe size

Women Men

German Shoe Size German Shoe Size

7% 66%

Age Distribution of German Beer Drinkers


Image Source: http://www.research-results.de/cms/upload/Fachartikel/2005/Ausgabe7/05-07-34-Bild2.jpg

Plus other more complicated selection effects for customers of a beer brewery ag

e gr

oup

number of beer drinkers [millions]

Turning things around – measuring selection functions to characterize the customer base


With enough aggregated data one can determine the average characteristic profile of the customers buying a specific product. What is special and characteristic about the ensemble of people buying

!   an Apple iPad Pro? !   a Ferrari?

!   old-fashioned music records? !   Birkenstock shoes?

!   super-fair-trade-bio-products?

This knowledge can then be used for e.g. improved targeted marketing

campaigns.


The ignorance of sample biases, selection effects,

and systematic errors may lead to wrong or even

inverted conclusions and results!

Take Home Message VI

Measured masses and sizes of objects selected based on their light emission properties


More Information: https://en.wikipedia.org/wiki/Malmquist_bias Image Source: J.-B. Melin et al. 2011

distance

mas

s &

siz

e

Observations show that on average objects are more massive and bigger the further they are away

Measured masses and sizes of objects selected based on their light emission properties


distance

mas

s &

siz

e

Observations show that on average objects are more massive and bigger the further they are away. This is a pure observational selection effect due to the inverse-square-law of light sources with distance, the so-called Malmquist Bias.

More Information: https://en.wikipedia.org/wiki/Malmquist_bias Image Source: J.-B. Melin et al. 2011

objects not detectable, i.e. not observable

The Fingers-of-God-Effect


More Information: https://en.wikipedia.org/wiki/Redshift-space_distortions Image Source: https://ned.ipac.caltech.edu/level5/Sept03/Peacock/Figures/figure1.jpg

X

Fingers-of-God The recession velocities of galaxies at cosmological distances are proportional to their distance. Plotting measured galaxy velocities against sky positions shows radial features in the spatial distribution all pointing towards our location, the so-called Fingers-of-God.

The Fingers-of-God-Effect


More Information: https://en.wikipedia.org/wiki/Redshift-space_distortions Image Source: https://ned.ipac.caltech.edu/level5/Sept03/Peacock/Figures/figure1.jpg

X

Fingers-of-God The recession velocities of galaxies at cosmological distances are proportional to their distance. Plotting measured galaxy velocities against sky positions shows radial features in the spatial distribution all pointing towards our location, the so-called Fingers-of-God. This is a systematic effect caused by galaxy clusters, which add an additional radial velocity component to the measured velocities owing to random motions in the gravitational field of the galaxy cluster.

Systematic Errors and Selection Effects are often not properly accounted for


Next time you read something like: A study has shown that…

…eating 1kg of Nutella every day decreases the probability of a heart attach

by 41.2% or

…10 kisses per day increase your life expectancy by

4.3 years

Systematic Errors and Selection Effects are often not properly accounted for


Next time you read something like: you should actually think A study has shown that…

…eating 1kg of Nutella every day decreases the probability of a heart attack

by 41.2 (±0.3 statistical, ±30 systematic error)% or

…10 kisses per day increase your life expectancy by

4.3 (±0.2 statistical, ±4 systematic error) years

Accuracy versus Precision


Image Source: https://en.wikipedia.org/wiki/Accuracy_and_precision

systematic

statistical

true


Statistical precision does not help

if the accuracy is totally off.

Take Home Message VII

Smart City & Smart Factory Think Tank


What Smart Data application or service would you like see in the near future

in the context of Smart Cities or Smart Factories?


Smart Data will change our everyday lives in the

future. However, intelligent analyses are the

prerequisites for everything to come!

Take Home Message VIII

Your Data. Your Potential.


Tel: +49 89 548 425 20 Email: [email protected] Web: www.omegalambdatec.com

smart data fo the smart cities and smart factories in the future

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