Event Detection

90f7f6e0fe6a8b894481158315b1de07?s=47 Marco Sero
October 19, 2012

Event Detection

This project is the thesis work for my bachelor degree at the Department of Computer Science of University of Turin.

90f7f6e0fe6a8b894481158315b1de07?s=128

Marco Sero

October 19, 2012
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Transcript

  1. EVENT DETECTION A system to detect geo-tagged events in real-time

    through the analysis of social datas. Università degli Studi di Torino Department of Computer Science Graduate: Marco Sero Supervisor: Prof. Giancarlo Ruffo
  2. MOBILE SOCIAL NETWORKING •  Leading social networks such as Facebook

    and Twitter are used mainly by mobile •  There is a bunch of other social networks born only for mobile (Instagram, Foursquare, Path)
  3. GEOTAG •  Thanks to mobile, the main part of these

    datas is georeferenced with accurate GPS coordinate, the geotag •  Moreover, very often photos and tweets have one or more key-words, the #hashtag + HASHTAG
  4. SOCIAL NETWORK and EVENTS

  5. PROJECT To join tweets and photos with the same hashtag

    and geographically close so as to detect social georeferenced events in real time
  6. HOW IT WORKS Reading from real-time endpoints Algorithm to detect

    the events Events
  7. THE CLUSTERING ALGORITHM •  Undefined Clustering •  Noise •  Cons:

    - no concurrency DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  8. •  MapReduce: programming paradigm to support concurrent and distributed computation

    •  Hadoop: framework to implement MapReduce applications MAP MAP MAP INPUT REDUCE   REDUCE   OUTPUT   (K, V) MAPREDUCE and HADOOP
  9. SIMULATION MAP MAP MAP MAP MAP REDUCE   REDUCE  

    MAP MAP #london2012 51, 0 #storm 40, -73 #storm 40, -73 #storm 40, -73 #london2012 51, 0 #london2012 51, 0 #london2012 51, 0 #storm 40, -73 #london2012 51, 0
  10. MAP function map(P, eps, MinPts) if P is unvisited then

    mark P as visited NeighborPts = regionQuery(P, eps) if sizeof(NeighborPts) < MinPts then do nothing else mark P as clusterized prepare the key create new cluster C C.neighborPoints = NeighborPts C.points = P emit(key, C) Creation of the new cluster Search neighborhood
  11. REDUCE function reduce(key, clusters, eps, MinPts) finalC is the final

    cluster for all C in clusters do finalC.points = finalC.points ∪ C.points for all P in C.neighborPoints do if P′ is not visited then mark P′ as visited NeighborPts′ = regionQuery(P′,eps) if sizeof(NeighborPts′) ≥ MinPts then NeighborPts = NeighborPts ∪ NeighborPts′ end if end if if P′ is not yet member of any cluster then add P′ to cluster finalC end if Clusters in input Neighborhood analysis for each cluster Cluster expansion Merge of points
  12. THE CHOICE OF THE DATABASE •  GIS (Geographic Information System)

    •  Speed •  Scalability •  Open source
  13. iOS APPLICATION

  14. iOS APPLICATION •  View the events both on the map

    or on the list •  Scan social networks for each event •  Share the events
  15. iOS APPLICATION •  View photos •  View tweets •  Comment

    •  Like •  Reply •  Retweet
  16. Thanks.