1. Challenging the Internet of the
Future with Urban Computing
Lecturer:
Emanuele Della Valle
emanuele.dellavalle@cefriel.it
http://swa.cefriel.it
http://emanueledellavalle.org
Authors:
Emanuele Della Valle, Irene Celino, Kono Kim, Zhisheng Huang,
Volker Tresp, Werner Hauptmann, and Yi Huang
2. Cities are alive
Cities born, grow,
evolve like living
beings.
The state of a city
changes
continuously,
influenced by a lot of
factors,
2
OneSpace
human ones: people
moving in the city or
extending it
natural ones:
precipitations or
climate changes
[source http://www.citysense.com]
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3. Urban Computing as a Way to Address them
[source IEEE Pervasive Computing,July-September 2007 (Vol. 6, No. 3)]
3
OneSpace
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4. Availability of Data
Some years ago, due to the lack of data, solving
Urban Computing problems with ICT looked like a
Sci-Fi idea.
Nowadays, a large amount of the required information
can be made available on the Internet at almost no
cost:
We are running a survey (please contribute), see
4
maps with the commercial activities and meeting places,
events scheduled in the city and their locations,
average speed in highways, but also normal streets
positions and speed of public transportation vehicles
parking availabilities in specific parking areas,
and so on.
OneSpace
http://wiki.larkc.eu/UrbanComputing/ShowUsABetterWay
http://wiki.larkc.eu/UrbanComputing/OtherDataSources
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5. The LarKC project
c.eu !!
/ ww.lark u
::/www.larkc.e
Visit http /w
Visit http /
[Source: Fensel, D., van Harmelen, F.: Unifying reasoning and search to web scale. IEEE Internet Computing 11(2) (2007)]
5
OneSpace
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6. A Challenging Use Case 1/5
Actors:
Varese
Carlo: a citizen
living in Varese.
The day after, he
has to go to
Lombardy Region
premises in
Milano at 11.00.
UCS: a fictitious
Urban Computing
System of Milano
area
Ways to Milano
Private Car
FS railways
Le Nord railways
6
OneSpace
Milano
For more information visit http://wiki.larkc.eu/UrbanComputing
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7. Vision for Urban Computing
Mobility
Tourism
City Planning
7
OneSpace
Culture
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8. Thank you for paying attention
Any Questions?
8
OneSpace
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9. Challenging the Internet of the
Future with Urban Computing
Lecturer:
Emanuele Della Valle
emanuele.dellavalle@cefriel.it
http://swa.cefriel.it
http://emanueledellavalle.org
Authors:
Emanuele Della Valle, Irene Celino, Kono Kim, Zhisheng Huang,
Volker Tresp, Werner Hauptmann, and Yi Huang
10. Insights – Analysis – Content Engineering
When Big Data and Predictive
Analytics Collide:
Visual Magic Happens
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11. The Problem:
Massive data explosion (mobile, social,
wearable, cloud, m2m etc.) and brands are
struggling to make use of this data.
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12. For more information visit http://wiki.larkc.eu/UrbanComputing
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13. Predictive Analytics
Predictive Analytics enables decision makers
to predict future events and proactively act on that
insight to drive better business.
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14. Predictive Analytics
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15. Then, Now & Where We’re going
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16. For more information visit http://wiki.larkc.eu/UrbanComputing
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17. Most Common Predictive Models
• Clustering – finding groups and predicting themes
• Classification – most popular “Decision tree”
• Association – multi assurance connected buckets
• Link Analysis – relationships
• Text Mining – unstructured data to meaning
• Time Series – predicting a continuous value
• Graph Structure – structure predicts behavior
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18. Where We’re Going – Pattern prediction
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19. Where to go
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20. KDD-Nuggets http://kdnuggets.com
RapidMiner http://rapid-i.com
R Statistical Computing http://www.r-project.org
Revolution Analytics http://www.revolutionanalytics.com
Teradata http://www.teradata.com
Tableau http://tableausoftware.com
Spotfire http://spotfire.tibco.com
SAS http://www.sas.com
IBM SPSS http://www.ib.com/software/analytics/spss
Mahout
https://cwiki.apahce.org/confluence/display/MAHOUT/Algoriths
Weka Open Source Data mining
http://www.cs.waikato.ac.nz/ml/weka
Pajek and (large) network analysis and visualization.
http://webdatacommons.org/hyperlinkgraph
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22. For more information visit http://wiki.larkc.eu/UrbanComputing
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23. Content Marketing Flow = data
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24. Visual Content Hub
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25. For more information visit http://wiki.larkc.eu/UrbanComputing
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26. For more information visit http://wiki.larkc.eu/UrbanComputing
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27. We’re Here to Help You
Great
Social Engagement
Is About
Knowing what
drives engagement
@chasemcmichael sales@infinigraph.com
@infinigraph
http://smo.infinigraph.com
http://www.infinigraph.com
YouTube /infinigraph
Slideshare /infinigraph
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29. Degree
The degree of vertex in an undirected graph is the
number of
edges incident to that vertex.
A vertex with degree one is called pendent vertex or
end
vertex.
A vertex with degree zero and hence has no incident
A
edges is
V1
called an isolated vertex.
B
Pendent vertex
Isolated vertex
In the undirected graph vertex v3 has the degree 3
And vertex v2 has the degree 2
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30. Verifying Isomorphic graph
Graph B
Graph A
Vertices(A) :
a
b
c
d
e
Vertices(B):
q
p
r
s
t
Degree of
vertices:
2
3
3
3
1
Edges(A):
e1
e2
e3
e4
e5
e6
Edges(B):
e’1
e’4
e’3
e’2
e’5
e’6
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31.
32. For more information visit http://wiki.larkc.eu/UrbanComputing
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33. For more information visit http://wiki.larkc.eu/UrbanComputing
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34. For more information visit http://wiki.larkc.eu/UrbanComputing
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35. For more information visit http://wiki.larkc.eu/UrbanComputing
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36. For more information visit http://wiki.larkc.eu/UrbanComputing
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37. Do you think that only one
specie can live in a habitat?
NO - many species can
live in the same habitat
What are species ?
Species are often defined as
a group of organisms capable
of interbreeding and
producing fertile offspring.
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38. What is a population?
Group of organisms
of the same specie
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39. C o m m u n i t y
It is a group of populations living together and
interacting with each other - sharing the same
food, places, shelter, water resources, etc, etc . . .
ReeF
Fores
t
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40. > That is an Ecosystem <
YEAH
!
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41. For more information visit http://wiki.larkc.eu/UrbanComputing
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42. Limiting Factors
A factor or limiting resource is a factor that controls a process, such
as organism growth or species population, size or distribution. The
availability of food, predation pressure, hard temperatures or
availability of shelter are examples of factors that could be limiting
for an organism. An example of a limiting factor is sunlight, which is
crucial in rainforests.
Another example is rain, which can bust an ecosystem in two ways.
One way is rain can destroy an ecosystem is flood. Flooding can wash
away shelter, food, and even parts of the life-form's population itself.
The other way rain can destroy an ecosystem is drought. The main way
it can destroy an ecosystem is the depletion of food sources.
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44. LIVING THINGS AND THE
ENVIRONMENT
In this unit we
will study the different rolls
and impact that the living
things have on the
environment. There will be a
strong focus in the
interaction that organisms
OBJECTIVE :
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45. How is a cat similar and different from a
fish besides the physical appearance ?
Organisms that live in
different habitats
el “ Gato volador ”
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46. What is an
Organism ?
It’s a Living thing that has (or can develop)
the ability to act or function independently
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47. Where do organisms
live?
They live in their habitats
What is a
habitat?
It is the physical space that has all the
propped conditions for an organism to live,
and reproduce. It has to provide the
necessary food and water needed to
survive
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48. The Dow’s 5 Most Loved
Stocks
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Editor's Notes
InfiniGraph solves a problem for marketers enveloped in massive amounts of data by enabling them to identify what is truly relevant to their customers.
We simplify trend identification, enable the production of higher quality content, and empower brands to create better engagement.
InfiniGraph is like the Comscore / Nielsen for engagement performance and competitive intelligence
Companies are fighting for attention and fighting blind. Massive explosion of data as more consumer engage on content over many networks. Brands need to know what’s working what’s relevant and be shown what’s trending. They need to know more than ever what’s effective. What their target audience is finding relevant and more importantly what’s driving the consumer to act on what content that turns into sales.
Decision making and the techniques and technologies to support and automate it will be the next competitive battleground for organizations.
Those who are using business rules, data mining, analytics and optimization today are the shock troops of the next wave of business innovation Tom Davenport Competing on Analytics
Extrapolating relationships around data and past events to create a statistical model for predicting future event.
Automating the discovery of patterns and connect the dots with past, present and future
InfiniGraph has invested over 3 years of data organization/collection on &gt;250K brands categorized into Industry segments. Scoring trillions of post over may content types give brands the right information to understand what’s driving engagement. This is a big deal and the historic data isn’t available unless you captured it. Brands need this level to extract the right insights InfiniGraph provides. The social graph is a mess with massive unstructured data, brands must have content scoring and analytics to measure what’s working in their industry NOT JUST THEMSELVES (what happens on your brands pages). Before you start developing a content strategy first step is brands need to know what their target audience is collectively doing and on what. Insights are automatically generated along with content curation feeds provides a brand ongoing intelligence used on every marketing initiatives. The toll creates highly strategic as well as tactical data ongoing.
Place content where the customers are at. Example of trending content on purchase pages or company blog page.
Everyone has a genius moment you’re just not having them every day. But around us there are continuous genius moments happening all the time. InfiniGraph taps that genius