SlideShare a Scribd company logo
1 of 17
Download to read offline
MK99 – Big Data 1 
Big data & cross-platform analytics 
MOOC lectures Pr. Clement Levallois
MK99 – Big Data 2 
Data & Localization
MK99 – Big Data 3 
Data & Localization 
1. 
Localization -> new dimensions! 
2. 
Maps, maps, maps 
3. 
Geospatial data and the need for new data structures 
4. 
Two companions: personalization and real time 
5. 
Territories: data is local 
6. 
Distributed systems: beyond local? 
Looking at localization in different ways
MK99 – Big Data 4 
1. New dimensions 
• 
Localization connects activities to physical space 
• 
This adds at least 4 interesting dimensions to data 
Place: Where is this activity happening? 
Distance: Are these two agents neighbors? 
Movement: Is this agent travelling? 
(together with speed and acceleration) 
Structure: How are these agents and activities configured in space?
MK99 – Big Data 5 
Example 1 
• 
Facebook new ad feature: 
– 
“Helping Local Businesses Reach More Customers” 
– 
Target ads to people living in a radius around your store. 
– 
Can also target people who have been recently in this radius. 
– 
https://www.facebook.com/business/news/facebook-local-awareness
MK99 – Big Data 6 
Example 2 
• 
Lyon Smart Data 
– 
An initiative by the city of Lyon 
– 
Making data open to foster innovation for citizens and businesses 
– 
Includes many datasets with geographical relevance 
– 
Similar initiatives in large cities: 
• 
Beijing City Lab
MK99 – Big Data 7 
2. Maps, maps, maps 
• 
Maps speed up understanding 
– 
Maps make data understandable by a wide audience 
– 
All visible at once, while zoom allows for details as well 
– 
Multiple information layers (colors, symbols, …) 
• 
Keep in mind: maps are always political 
– 
Watch this extract from the TV series "The West Wing“, Season 2, Episode 16: 
https://www.youtube.com/watch?v=vVX-PrBRtTY
MK99 – Big Data 8 
Example 
• 
Every single building of the Netherlands on a map 
• 
Colored by year of construction 
• 
With function (retail or housing?) and surface highlighted 
• 
Zoomable and draggable. 
The city center of Leiden: http://code.waag.org/buildings/
MK99 – Big Data 9 
Key resources in map-making 
• 
Stamen 
– 
Agency based in San Francisco 
– 
Hire them or check their work 
• 
Mapbox.com 
– 
SaaS to create interactive maps in web pages and mobile apps. 
• 
OpenStreetMap 
– 
A crowd sourced open source map of the world. Available through API.
MK99 – Big Data 10 
3. How to represent “space” in data format? 
• 
Traditionally stored in tables in relational databases, queried with SQL. 
• 
Query on the table, then exported to a Geographical Information System (GIS) for representation and analysis 
– 
Leaders: ArcGIS an QGIS 
• 
Problem at the query stage. How to ask to extract these data from the table? 
– 
« Return all customers living between point A and B » 
– 
« List all customers who live at less than one mile from each other » 
-> Traditional relational databases, which are made of tables like the one above, cannot process this kind of query efficiently. 
Customer 
Address 
Customer 1 
67 Pelikaanstraar, Leiden 2314 CR 
Customer 2 
12 Breestraat, Rotterdam 3046 DM
MK99 – Big Data 11 
Emerging solutions to work with space 
1. 
SQL solutions 
– 
Microsoft SQL server since 2008 
• 
Possible to store and query “geometric” and “geographic” objects 
• 
Possible to use complex queries on these objects 
2. 
NoSQL solutions 
– 
CartoDB: specializing in geospatial data + mapping. 
– 
Neo4J Spatial enables to mix the logics of networks with places in the data, so that you can make such queries on your data: 
• 
"Select all streets in the Municipality of NYC where at least 2 of my friends are walking right now." 
3. 
Javascript leading the way! 
– 
GeoJSon and TopoJSon: 2 data formats to represent geometric and geographic data developed for Javascript applications – and beyond.
MK99 – Big Data 12 
4. Two friends for localization: personalization and real-time 
• 
Knowing the person, its location, at a precise time unlocks meaningful push notifications 
• 
Push notifications are these alerts sent by an app on your mobile, visible as transient icons. 
• 
Gets “push marketing” back on solid foundations: 
– 
Push marketing actions only to the right person, at the right place, at the right time (and at the right frequency!) 
You’ve got mail!
MK99 – Big Data 13 
Now for different (opposite?) approaches to localization 
• 
Territories 
– 
Not just people are localized. Data is local, too. 
• 
Distributed systems 
– 
Some projects attempt to build completely decentralized systems of transactions, functioning freely and immune from local regulations. 
≠
MK99 – Big Data 14 
5. Localization is about people and territories 
Data is a fungible and universal material (just 0s and 1s) 
and yet … 
The logic of territories is shaping data: there is a geography of data. 
Cultural, social, political, linguistic, economic dimensions to data. 
Frederic Martel 
Published in French in 2014, 
Available in English in 2015.
MK99 – Big Data 15 
Examples 
• 
Data protection: not all countries are equal 
– 
http://www.darkreading.com/cloud/privacy-security-and- the-geography-of-data-protection-/a/d-id/1315480 
• 
Data handling devices 
– 
India and Africa have ++ share of mobile devices 
• 
Data production 
– 
The uneven geography of Mechanical Turk
MK99 – Big Data 16 
6. Distributed systems – the end of territories? 
• 
Libertarian dream of the cypher-punks: 
– 
Individuals transact without consideration of their nationality, currency, legal system, political regime. 
• 
Bitcoin 
– 
the currency for these transactions? 
• 
Torrents 
– 
The exchange platform for numeric goods? 
• 
Ethereum 
– 
the platform where these transactions are created and exchanged? 
• 
In practice 
– 
Organizations, banking, voting systems, … any aggregated human activity could emerge without reference to local territories or institutions. Just groups of individuals transacting voluntarily and securely.
MK99 – Big Data 17 
This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) 
Contact Clement Levallois (levallois [at] em-lyon.com) for more information.

More Related Content

More from Clement Levallois

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsClement Levallois
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielleClement Levallois
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications businessClement Levallois
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Clement Levallois
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginnersClement Levallois
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomClement Levallois
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le businessClement Levallois
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for businessClement Levallois
 
A Primer on Text Mining for Business
A Primer on Text Mining for BusinessA Primer on Text Mining for Business
A Primer on Text Mining for BusinessClement Levallois
 
The business stakes of data integration
The business stakes of data integrationThe business stakes of data integration
The business stakes of data integrationClement Levallois
 

More from Clement Levallois (14)

Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accountsPart 2: covid-19 on Twitter, with a focus on 3 new seed accounts
Part 2: covid-19 on Twitter, with a focus on 3 new seed accounts
 
Education et intelligence artificielle
Education et intelligence artificielleEducation et intelligence artificielle
Education et intelligence artificielle
 
3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business3 familles d'intelligence artificielle et leurs applications business
3 familles d'intelligence artificielle et leurs applications business
 
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
Présentation FrenchWeb: Qu'est-ce que la visualisation des données?
 
Presentation of programming languages for beginners
Presentation of programming languages for beginnersPresentation of programming languages for beginners
Presentation of programming languages for beginners
 
Umigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroomUmigon: crowdsourcing in the classroom
Umigon: crowdsourcing in the classroom
 
Data visualization: enjeux pour le business
Data visualization: enjeux pour le businessData visualization: enjeux pour le business
Data visualization: enjeux pour le business
 
Twitter for beginners
Twitter for beginnersTwitter for beginners
Twitter for beginners
 
An explanation of machine learning for business
An explanation of machine learning for businessAn explanation of machine learning for business
An explanation of machine learning for business
 
Data and personalization
Data and personalizationData and personalization
Data and personalization
 
A Primer on Text Mining for Business
A Primer on Text Mining for BusinessA Primer on Text Mining for Business
A Primer on Text Mining for Business
 
The business stakes of data integration
The business stakes of data integrationThe business stakes of data integration
The business stakes of data integration
 
What is big data?
What is big data?What is big data?
What is big data?
 
What is "data"?
What is "data"?What is "data"?
What is "data"?
 

Recently uploaded

Case study on tata clothing brand zudio in detail
Case study on tata clothing brand zudio in detailCase study on tata clothing brand zudio in detail
Case study on tata clothing brand zudio in detailAriel592675
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607dollysharma2066
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaoncallgirls2057
 
Independent Call Girls Andheri Nightlaila 9967584737
Independent Call Girls Andheri Nightlaila 9967584737Independent Call Girls Andheri Nightlaila 9967584737
Independent Call Girls Andheri Nightlaila 9967584737Riya Pathan
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfJos Voskuil
 
Global Scenario On Sustainable and Resilient Coconut Industry by Dr. Jelfina...
Global Scenario On Sustainable  and Resilient Coconut Industry by Dr. Jelfina...Global Scenario On Sustainable  and Resilient Coconut Industry by Dr. Jelfina...
Global Scenario On Sustainable and Resilient Coconut Industry by Dr. Jelfina...ictsugar
 
Islamabad Escorts | Call 03070433345 | Escort Service in Islamabad
Islamabad Escorts | Call 03070433345 | Escort Service in IslamabadIslamabad Escorts | Call 03070433345 | Escort Service in Islamabad
Islamabad Escorts | Call 03070433345 | Escort Service in IslamabadAyesha Khan
 
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...lizamodels9
 
MAHA Global and IPR: Do Actions Speak Louder Than Words?
MAHA Global and IPR: Do Actions Speak Louder Than Words?MAHA Global and IPR: Do Actions Speak Louder Than Words?
MAHA Global and IPR: Do Actions Speak Louder Than Words?Olivia Kresic
 
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In.../:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...lizamodels9
 
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...lizamodels9
 
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...lizamodels9
 
8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCRashishs7044
 
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCRashishs7044
 
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...lizamodels9
 
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu Menza
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu MenzaYouth Involvement in an Innovative Coconut Value Chain by Mwalimu Menza
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu Menzaictsugar
 
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort ServiceCall US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Servicecallgirls2057
 
International Business Environments and Operations 16th Global Edition test b...
International Business Environments and Operations 16th Global Edition test b...International Business Environments and Operations 16th Global Edition test b...
International Business Environments and Operations 16th Global Edition test b...ssuserf63bd7
 
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCRashishs7044
 

Recently uploaded (20)

Case study on tata clothing brand zudio in detail
Case study on tata clothing brand zudio in detailCase study on tata clothing brand zudio in detail
Case study on tata clothing brand zudio in detail
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
 
Independent Call Girls Andheri Nightlaila 9967584737
Independent Call Girls Andheri Nightlaila 9967584737Independent Call Girls Andheri Nightlaila 9967584737
Independent Call Girls Andheri Nightlaila 9967584737
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdf
 
Global Scenario On Sustainable and Resilient Coconut Industry by Dr. Jelfina...
Global Scenario On Sustainable  and Resilient Coconut Industry by Dr. Jelfina...Global Scenario On Sustainable  and Resilient Coconut Industry by Dr. Jelfina...
Global Scenario On Sustainable and Resilient Coconut Industry by Dr. Jelfina...
 
Islamabad Escorts | Call 03070433345 | Escort Service in Islamabad
Islamabad Escorts | Call 03070433345 | Escort Service in IslamabadIslamabad Escorts | Call 03070433345 | Escort Service in Islamabad
Islamabad Escorts | Call 03070433345 | Escort Service in Islamabad
 
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...
Call Girls In Connaught Place Delhi ❤️88604**77959_Russian 100% Genuine Escor...
 
MAHA Global and IPR: Do Actions Speak Louder Than Words?
MAHA Global and IPR: Do Actions Speak Louder Than Words?MAHA Global and IPR: Do Actions Speak Louder Than Words?
MAHA Global and IPR: Do Actions Speak Louder Than Words?
 
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In.../:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
 
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...
Lowrate Call Girls In Sector 18 Noida ❤️8860477959 Escorts 100% Genuine Servi...
 
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...
Call Girls In Radisson Blu Hotel New Delhi Paschim Vihar ❤️8860477959 Escorts...
 
8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR
 
Corporate Profile 47Billion Information Technology
Corporate Profile 47Billion Information TechnologyCorporate Profile 47Billion Information Technology
Corporate Profile 47Billion Information Technology
 
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
 
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
 
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu Menza
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu MenzaYouth Involvement in an Innovative Coconut Value Chain by Mwalimu Menza
Youth Involvement in an Innovative Coconut Value Chain by Mwalimu Menza
 
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort ServiceCall US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
 
International Business Environments and Operations 16th Global Edition test b...
International Business Environments and Operations 16th Global Edition test b...International Business Environments and Operations 16th Global Edition test b...
International Business Environments and Operations 16th Global Edition test b...
 
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
 

Data and localization

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 Data & Localization
  • 3. MK99 – Big Data 3 Data & Localization 1. Localization -> new dimensions! 2. Maps, maps, maps 3. Geospatial data and the need for new data structures 4. Two companions: personalization and real time 5. Territories: data is local 6. Distributed systems: beyond local? Looking at localization in different ways
  • 4. MK99 – Big Data 4 1. New dimensions • Localization connects activities to physical space • This adds at least 4 interesting dimensions to data Place: Where is this activity happening? Distance: Are these two agents neighbors? Movement: Is this agent travelling? (together with speed and acceleration) Structure: How are these agents and activities configured in space?
  • 5. MK99 – Big Data 5 Example 1 • Facebook new ad feature: – “Helping Local Businesses Reach More Customers” – Target ads to people living in a radius around your store. – Can also target people who have been recently in this radius. – https://www.facebook.com/business/news/facebook-local-awareness
  • 6. MK99 – Big Data 6 Example 2 • Lyon Smart Data – An initiative by the city of Lyon – Making data open to foster innovation for citizens and businesses – Includes many datasets with geographical relevance – Similar initiatives in large cities: • Beijing City Lab
  • 7. MK99 – Big Data 7 2. Maps, maps, maps • Maps speed up understanding – Maps make data understandable by a wide audience – All visible at once, while zoom allows for details as well – Multiple information layers (colors, symbols, …) • Keep in mind: maps are always political – Watch this extract from the TV series "The West Wing“, Season 2, Episode 16: https://www.youtube.com/watch?v=vVX-PrBRtTY
  • 8. MK99 – Big Data 8 Example • Every single building of the Netherlands on a map • Colored by year of construction • With function (retail or housing?) and surface highlighted • Zoomable and draggable. The city center of Leiden: http://code.waag.org/buildings/
  • 9. MK99 – Big Data 9 Key resources in map-making • Stamen – Agency based in San Francisco – Hire them or check their work • Mapbox.com – SaaS to create interactive maps in web pages and mobile apps. • OpenStreetMap – A crowd sourced open source map of the world. Available through API.
  • 10. MK99 – Big Data 10 3. How to represent “space” in data format? • Traditionally stored in tables in relational databases, queried with SQL. • Query on the table, then exported to a Geographical Information System (GIS) for representation and analysis – Leaders: ArcGIS an QGIS • Problem at the query stage. How to ask to extract these data from the table? – « Return all customers living between point A and B » – « List all customers who live at less than one mile from each other » -> Traditional relational databases, which are made of tables like the one above, cannot process this kind of query efficiently. Customer Address Customer 1 67 Pelikaanstraar, Leiden 2314 CR Customer 2 12 Breestraat, Rotterdam 3046 DM
  • 11. MK99 – Big Data 11 Emerging solutions to work with space 1. SQL solutions – Microsoft SQL server since 2008 • Possible to store and query “geometric” and “geographic” objects • Possible to use complex queries on these objects 2. NoSQL solutions – CartoDB: specializing in geospatial data + mapping. – Neo4J Spatial enables to mix the logics of networks with places in the data, so that you can make such queries on your data: • "Select all streets in the Municipality of NYC where at least 2 of my friends are walking right now." 3. Javascript leading the way! – GeoJSon and TopoJSon: 2 data formats to represent geometric and geographic data developed for Javascript applications – and beyond.
  • 12. MK99 – Big Data 12 4. Two friends for localization: personalization and real-time • Knowing the person, its location, at a precise time unlocks meaningful push notifications • Push notifications are these alerts sent by an app on your mobile, visible as transient icons. • Gets “push marketing” back on solid foundations: – Push marketing actions only to the right person, at the right place, at the right time (and at the right frequency!) You’ve got mail!
  • 13. MK99 – Big Data 13 Now for different (opposite?) approaches to localization • Territories – Not just people are localized. Data is local, too. • Distributed systems – Some projects attempt to build completely decentralized systems of transactions, functioning freely and immune from local regulations. ≠
  • 14. MK99 – Big Data 14 5. Localization is about people and territories Data is a fungible and universal material (just 0s and 1s) and yet … The logic of territories is shaping data: there is a geography of data. Cultural, social, political, linguistic, economic dimensions to data. Frederic Martel Published in French in 2014, Available in English in 2015.
  • 15. MK99 – Big Data 15 Examples • Data protection: not all countries are equal – http://www.darkreading.com/cloud/privacy-security-and- the-geography-of-data-protection-/a/d-id/1315480 • Data handling devices – India and Africa have ++ share of mobile devices • Data production – The uneven geography of Mechanical Turk
  • 16. MK99 – Big Data 16 6. Distributed systems – the end of territories? • Libertarian dream of the cypher-punks: – Individuals transact without consideration of their nationality, currency, legal system, political regime. • Bitcoin – the currency for these transactions? • Torrents – The exchange platform for numeric goods? • Ethereum – the platform where these transactions are created and exchanged? • In practice – Organizations, banking, voting systems, … any aggregated human activity could emerge without reference to local territories or institutions. Just groups of individuals transacting voluntarily and securely.
  • 17. MK99 – Big Data 17 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.