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  • 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 >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
  • EXAMPLE OF POPULATED SLIDE

Transcript

  • 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] For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 3. Urban Computing as a Way to Address them [source IEEE Pervasive Computing,July-September 2007 (Vol. 6, No. 3)] 3 OneSpace For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 7. Vision for Urban Computing Mobility Tourism City Planning 7 OneSpace Culture For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 8. Thank you for paying attention Any Questions? 8 OneSpace For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 11. The Problem: Massive data explosion (mobile, social, wearable, cloud, m2m etc.) and brands are struggling to make use of this data. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 12. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 13. Predictive Analytics Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 14. Predictive Analytics For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 15. Then, Now & Where We’re going For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 16. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 18. Where We’re Going – Pattern prediction For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 19. Where to go For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 21.  Tableau Demo http://public.tableausoftware.com/views/Predictive DataVisualizationwithSSASDataMining/Classificati on#1 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 22. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 23. Content Marketing Flow = data For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 24. Visual Content Hub For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 25. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 26. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 28. Graphs Part-II
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 31. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 32. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 33. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 34. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 35. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 36. 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. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 37. What is a population? Group of organisms of the same specie For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 38. 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 39. > That is an Ecosystem < YEAH ! For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 40. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 41. 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. For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 42. Episode 01
  • 43. 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 : For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 44. How is a cat similar and different from a fish besides the physical appearance ? Organisms that live in different habitats el “ Gato volador ” For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 45. What is an Organism ? It’s a Living thing that has (or can develop) the ability to act or function independently For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 46. 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 For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project
  • 47. The Dow’s 5 Most Loved Stocks For more information visit http://wiki.larkc.eu/UrbanComputing Seven Framework Programme Information Society Technologies (IST) Specific Targeted Research Project