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3cixty - A New Platform for City Exploration

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Keynote talk given at the 2015 BMW Summer School - Connected Vehicles Driving on Digital Roads, http://www.bmwsummerschool.com/

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3cixty - A New Platform for City Exploration

  1. 1. 3cixty: a New Platform for City Exploration Raphaël Troncy <raphael.troncy@eurecom.fr> Multimedia Semantics, EURECOM @rtroncy … and many others who need to be credit: Giuseppe Rizzo, Houda Khrouf, Julien Plu, Ahmad Assaf, Oscar Corcho, Juan Carlos Ballesteros, José Luis Redondo Gardia, Ghislain Atemezing, Vuk Milicic, several students projects, etc.
  2. 2. 07/07/2015 - BMW Summer School - Lake Tegernsee - 2
  3. 3. The Central Challenge 07/07/2015 - BMW Summer School - Lake Tegernsee - 3
  4. 4. An Evening in Milan 07/07/2015 - BMW Summer School - Lake Tegernsee - 4
  5. 5. “I’d like to take a break from Expo and visit Milan. What’s the best time for a break, and what things in the city could I go to then?” Expo and the City 07/07/2015 - BMW Summer School - Lake Tegernsee - 5
  6. 6. Our Solution: Apps Powered by 3cixty Showcase app: ExplorMI 360 https://www.3cixty.com/ 07/07/2015 - BMW Summer School - Lake Tegernsee - 6
  7. 7. From Technology Maturation To Business  First showcase in Milan (2014-2015)  Next showcases planned in London, Nice, Bologna, Madrid (2015-2016) 07/07/2015 - BMW Summer School - Lake Tegernsee - 7
  8. 8. 07/07/2015 - BMW Summer School - Lake Tegernsee - 8
  9. 9. 3cixty: a smart city and big data project 07/07/2015 - BMW Summer School - Lake Tegernsee - 9
  10. 10.  Events  Places and Sights  Reviews and Ratings  Media  Transport  Urban Summaries 07/07/2015 - BMW Summer School - Lake Tegernsee - 10
  11. 11. … and the Web 07/07/2015 - BMW Summer School - Lake Tegernsee - 11
  12. 12. A lot of information… http://www.flickr.com/photos/mwparenteau/432039783 07/07/2015 - BMW Summer School - Lake Tegernsee
  13. 13. Searching for Tegernsee (Bavaria) 07/07/2015 - BMW Summer School - Lake Tegernsee - 13
  14. 14. Searching for Milan (Italy) or EXPO 2015 07/07/2015 - BMW Summer School - Lake Tegernsee - 14 https://en.wikipedia.org/wiki/ Knowledge_Graph
  15. 15. Google Knowledge Graph 07/07/2015 - BMW Summer School - Lake Tegernsee - 15
  16. 16. Bing - Satori 07/07/2015 - BMW Summer School - Lake Tegernsee - 16
  17. 17. 07/07/2015 - BMW Summer School - Lake Tegernsee wikidata:Q210022dbpedia:Expo_2015 - 17
  18. 18. 07/07/2015 - BMW Summer School - Lake Tegernsee Linked Data Principles  Tim Berners Lee [2006] (Design Issues) 1. Use URIs to identify things (anything, not just documents); 2. Use HTTP URIs – globally unique names, distributed ownership – so that people can look up those names; 3. Provide useful information in RDF – when someone looks up a URI; 4. Include RDF links to other URIs – to enable discovery of related information - 18
  19. 19. 3cixty Architecture 07/07/2015 - BMW Summer School - Lake Tegernsee Heterogeneous data sources Data Crawling Data Streams RDF Conversion RSS Update - 19
  20. 20. Knowledge Modeling: Transit 07/07/2015 - BMW Summer School - Lake Tegernsee - 20
  21. 21. Knowledge Graphs for Transportation 07/07/2015 - BMW Summer School - Lake Tegernsee - 21
  22. 22. Knowledge Graphs for Places / Events 07/07/2015 - BMW Summer School - Lake Tegernsee - 22
  23. 23. On the Importance of Having Good Maps 07/07/2015 - BMW Summer School - Lake Tegernsee - 23
  24. 24. On the Importance of Having Good Maps 07/07/2015 - BMW Summer School - Lake Tegernsee - 24
  25. 25. On the Importance of Having Good Maps 07/07/2015 - BMW Summer School - Lake Tegernsee - 25
  26. 26. 3cixty: a smart city and big data project 07/07/2015 - BMW Summer School - Lake Tegernsee - 26
  27. 27. Handling Highly Dynamic Data  Data streams  Live hotel rooms availability through the EAN network (Expedia)  Live position of the city buses in the city  Live state of bike sharing stations  Complex Event Processing: T-Rex / SPARQL Streams T-Rex TESLA rules Publishers Subscribers Primitive events Primitive or comp. events E015 pull-push adapter E015 services 07/07/2015 - BMW Summer School - Lake Tegernsee - 27
  28. 28. CEP with T-Rex  T-Rex is a high-performance CEP engine, providing an ad- hoc rule language (TESLA)  Examples of TESLA rules: Define GrowingDelay(t_id: string, delay: int) From TrainDelay(t_id => $t, delay => $d) as T1 and last TrainDelay(t_id=$t, $d>delay) as T2 within 5min from T1 Where GrowingDelay.t_id := T1.t_id, GrowingDelay.delay:=T1.delay Define LowBikeAvail(bike_stat: string, availNow: int, availAvg: int) From BikeMiStat(avail_bikes<5) as S Where bike_stat := S.stat_id, availNow := S.avail_bikes, availAvg := AVG(BikeMiStat.avail_bikes) within 60min from S 07/07/2015 - BMW Summer School - Lake Tegernsee - 28
  29. 29. 3cixty Architecture 07/07/2015 - BMW Summer School - Lake Tegernsee Real-time Reconciliation - Category mapping - Instance matching Heterogeneous data sources Data Crawling Data Streams RDF Conversion RSS Update - 29
  30. 30. Are those two venues the same? 07/07/2015 - BMW Summer School - Lake Tegernsee Google Places: name, address, geoLoc Yelp: name, address, geoLoc ➔ Slightly different POI name ➔ Different tel number ➔ Different locality (Milan or Pero?) ➔ Different region (Lombardia or MI?) ➔ Distance using the Harvesine formula: 2,428m ! - 30
  31. 31.  The events similarity is a mutual agreement of their factual properties  Based on top-k dependencies between properties Data reconciliation (learning to align) p1 p2 dependency title1 title2 0.30 place1 place2 0.28 title1 agent2 0.26 agent1 agent2 0.21 description1 title2 0.16 Minimal conditions to fetch similar events using SPARQL 1st level Refine the results 2nd level 07/07/2015 - BMW Summer School - Lake Tegernsee - 31
  32. 32. 3cixty Architecture 07/07/2015 - BMW Summer School - Lake Tegernsee Real-time Reconciliation - Category mapping - Instance matching Heterogeneous data sources Data Crawling Data Streams RDF Conversion RSS Update - 32 Web Applications SPARQL REST API (Elda)
  33. 33. ExplorMI 360 Web App 07/07/2015 - BMW Summer School - Lake Tegernsee - 33
  34. 34. ExplorMI 360 Mobile Guide (Android / iOS) 07/07/2015 - BMW Summer School - Lake Tegernsee - 34
  35. 35. Bringing your Wishlist with you 07/07/2015 - BMW Summer School - Lake Tegernsee - 35
  36. 36.  A Service for Executing Mixed-Domain Queries Execute queries in your app that combine diverse types of information  A “Parallel Exploration” Graphical UI Enable users to construct and save trees of interrelated queries that enable them to explore several aspects of the city simultaneously  A “Wish List” Service Allow users to indicate where they may want to go and (optionally) when Store this information in the cloud so that it can be accessed from any 3cixty app when the user is logged in 07/07/2015 - BMW Summer School - Lake Tegernsee - 36 Brief Descriptions of Services (1)
  37. 37. Brief Descriptions of Services (2)  A Mobility Profiling service Track users’ movements within the city, including their use of modes of transportation Enable users to make queries with restrictions like “in a location that I’ve never been to before” Display data from the user’s mobility profile along with other 3cixty information  Generic Crowdsourcing Platform Efficiently write effective interfaces that enable users to contribute information about aspects of the city access such information provided by others  A Social Network Mining Service Access what a user’s friends have done or said with regard to particular things in the city 07/07/2015 - BMW Summer School - Lake Tegernsee - 37
  38. 38. Mobility Profiling Service 07/07/2015 - BMW Summer School - Lake Tegernsee - 38
  39. 39. Data Mining in the Knowledge Graph 07/07/2015 - BMW Summer School - Lake Tegernsee - 39
  40. 40. Extracting Patterns in the Knowledge Graph  Geosummly: http://geosummly.eurecom.fr/ 07/07/2015 - BMW Summer School - Lake Tegernsee - 40 Zooming in “Shop & Service”
  41. 41. 1 2 n lat*,long* = latitude and longitude of the centroid of the cell(i). It allows to reduce the observation noise of the single venues, and to reduce the data set sparsity. 1 lat* lng* f(1,1) f(1,2 ) 2 3 ... n f(n,n) Grid Sampling on Foursquare 07/07/2015 - BMW Summer School - Lake Tegernsee - 41
  42. 42. Definitions:  eps : reachable distance. We use the Euclidean distance (points linked with arrows)  minPts : min number of points to have a cluster (given the example, it can be 1...8) Automatic parameter estimation:  eps : applying the Euclidean distance, we compute the mean Euclidean distance of the feature values in the grid  minPts : considering each feature independent each other, we assign to minPts the mean value observed in the entire grid, reduced a small quantity based on the law of large numbers lng lat Parameter Estimation 07/07/2015 - BMW Summer School - Lake Tegernsee - 42
  43. 43. Clustering Algorithm  We propose GeoSubClu, a density based clustering algorithm inspired by SubClu  Inputs:  eps, minPts  O = {o1 , . . . , om} of geographic objects located at the spatial coordinates fx and fy. Each object corresponds to the centroid of a cell in the sampled grid  F = {f1, …, fn} set of features. Each feature corresponds the observed frequency of a given category in the area, normalized intra-feature and per surface of the cell  Outputs:  S = {s1, …, sk} of k-dimensional subspaces sk. Each subspace corresponds to a cluster that has k prominent different features 07/07/2015 - BMW Summer School - Lake Tegernsee - 43
  44. 44. Extracting and Linking Entities  An hybrid approach which combines the strength of a linguistic-based method augmented by a high coverage in the annotation obtained by using a large knowledge base 07/07/2015 - BMW Summer School - Lake Tegernsee - 44
  45. 45. Extracting More Information from Reviews  Extracting sentiments: features engineering Pre-processing, emotion dictionary (DAL), POS, capitalization, time of the day, day of the week, weather, social graph SVM + kNN classifiers used jointly  Extracting sub-categories Japanese restaurant Ramen or Teppanyaki LDA approach BoW with 1-ngram 07/07/2015 - BMW Summer School - Lake Tegernsee - 45
  46. 46. Semantic and Machine Learning 07/07/2015 - BMW Summer School - Lake Tegernsee - 46 Lise Getoor - Combining Statistics and Semantics to Turn Data into Knowledge ESWC 2015 Keynote
  47. 47. Big Data is not Flat  It is multi-modal, multi-relational, spatio- temporal, multimedia Machine Learning needs knowledge graphs Knowledge Graphs needs machine learning Deep learning vs Features Engineering  Key idea: Statistical Relational Learning (SRL) Entity Resolution: determine which nodes refer to the same underlying real world object Link Prediction: infer the existence of new edges in the graph Classification: infer labels in a graph 07/07/2015 - BMW Summer School - Lake Tegernsee - 47
  48. 48. http://www.slideshare.net/troncy 07/07/2015 - BMW Summer School - Lake Tegernsee - 48

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