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Listening to the pulse of our cities with Stream Reasoning (and few more technologies)

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The digital reflection of our cities is sharpening and it is tracking their evolution with a decreasing delay. However, we risk that data piles up without easing decision making. This key note, which I gave at the 12th Semantic Web Summer School, presents how stream reasoning (an approach to tame simultaneously the variety and velocity dimensions of Big Data) and advance visual analytics can support decision makers and discusses the lesson learnt.

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Listening to the pulse of our cities with Stream Reasoning (and few more technologies)

  1. 1. Listening to the pulse of our cities with Stream Reasoning (and few more technologies) Emanuele Della Valle @manudellavalle - emanuele.dellavalle@polimi.it http://emanueledellavalle.org
  2. 2. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Share, Remix, Reuse — Legally  This work is licensed under the Creative Commons Attribution 3.0 Unported License.  Your are free: • to Share — to copy, distribute and transmit the work • to Remix — to adapt the work  Under the following conditions • Attribution — You must attribute the work by inserting – “[source http://emanueledellavalle.org]” at the end of each reused slide – a credits slide stating - These slides are partially based on “Listening to the pulse of our cities fusing Social Media Streams and Call Data Records” by Emanuele Della Valle  To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ 2
  3. 3. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Me  Assistant Professor at DEIB Politecnico di Milano  Expert in semantic technologies and stream computing  Brander of stream reasoning: an approach to master the velocity and variety dimension of Big Data  15 years experience in research and innovation projects  Startupper: fluxedo.com  R&D advisor: socialometers.com 3
  4. 4. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Acknowledgements  Politecnico di Milano • DEIB – What - Scientific direction - Semantic technologies - Stream Processing - Data science – Who - Emanuele Della Valle - Marco Balduini • Density Design Lab – What - Visual analytics – Who - Paolo Ciuccarelli - Matteo Azzi  Telecom Italia • SKIL Lab – What - Big Data technology - Data Science – Who - Fabrizio Antonelli - Roberto Larker  Funding agency 4
  5. 5. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Agenda  Context  Problem  Experimental setting  Solution  Evaluation  Conclusions 5
  6. 6. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 6 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
  7. 7. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 7 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg] because the urban environment is captured in open datasets
  8. 8. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 8 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg] and streams of information flows through our cities thanks to
  9. 9. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 9 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg] and streams of information flows through our cities thanks to the pervasive deployment of sensors
  10. 10. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 10 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg] and streams of information flows through our cities thanks to the wide adoption of smart phones
  11. 11. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The digital reflection of our cities is sharpening 11 [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg] and streams of information flows through our cities thanks to the usage of (location-based) social networks
  12. 12. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org and it tracks changes with a decreasing delay 12
  13. 13. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org and it tracks changes with a decreasing delay 13 Data source By when Frequency Delay Census data 100s year years months Newspaper 100s year days 1 day Weather sensors 10s year hours/minutes hours/minutes TV news 10s years hours minutes Traffic sensors years 15 minutes minutes Call Data Recors years 15 minutes hours Social media years seconds seconds IoT recently milliseconds milliseconds
  14. 14. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 14 Data piles up without easing decision making I have to decide: A or B? Why not C? What if D? mayor
  15. 15. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org But smarter Big Data can … …advance our ability to feel the pulse of our cities 15 fusing all those data sources making sense of the fused information mayor Definitely E! to improve decision making and deliver innovative services
  16. 16. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Can we collect, analyse and repurpose • social media and • Call Data Records to allow • perceiving emerging patterns and • observing their dynamics? Let's focus on a concrete research question 16 [photo: https://www.flickr.com/photos/debord/4932655275]
  17. 17. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Can we collect, analyse and repurpose • social media captured at place and events and • privacy-preserving aggregates of Call Data Records to allow visually • perceiving emerging patterns and • observing their dynamics? More precisely, the research question is 17 [photo: https://www.flickr.com/photos/debord/4932655275]
  18. 18. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How to set up an experiment? 18 [photo: https://www.flickr.com/photos/myfuturedotcom/6053042920] Question Answer Which city? Milan Comparing what? Milan Design Week vs. Milan in general Experimental subjects? Event Managers & casual audience
  19. 19. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org What's Milan Design Week? 19 [map: http://www.fuorisalone.it] The Milan Design Week (MDW) is a city-scale event • held yearly in Milan, • featuring around 1,200 events • in 500+ places spread across the city and • attracting about half a million people from all over the world.
  20. 20. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 20 CitySensing for event managers (2013) F. Antonelli, M.Azzi, M.Balduini, P.Ciuccarelli, E.Della Valle, R. Larcher: City sensing: visualising mobile and social data about a city scale event. AVI 2014: 337-338 http://jol.telecomitalia.com/jols kil/citysensing/
  21. 21. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 21 CitySensing for casual audience (2014) M.Balduini, E.Della Valle, M.Azzi, R.Larcher, F.Antonelli, and P.Ciuccarelli: CitySensing: Fusing City Data for Visual Storytelling. IEEE MultiMedia. http://jol.telecomitalia.com/jolskil/citysensing/ http://citysensing.fuorisalone.it/
  22. 22. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Ingredients of the proposed solution  Big Data technologies - Address "volume" of data that do not fit in memory - Address "velocity" of data streams in memory  semantic technologies - Address "variety" using Ontology Based Data Access - Named Entity Recognition and Linking  data science - Statistical modelling - Detecting anomalies  Visual analytics - Allow no-expert access to data - Tell stories out of data 22 StreamReasoning
  23. 23. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 23 What's Stream Reasoning? Tame Variety and Velocity simultaneously Traditional StreamReasoning E.Della Valle, S. Ceri, F. van Harmelen, D. Fensel: It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009) E. Della Valle, D. Dell'Aglio, A. Margara: Taming velocity and variety simultaneously in big data with stream reasoning: tutorial. DEBS 2016: 394-401
  24. 24. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 24 What's Stream Reasoning? Tame Variety and Velocity simultaneously Traditional StreamReasoning E.Della Valle, S. Ceri, F. van Harmelen, D. Fensel: It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009) E. Della Valle, D. Dell'Aglio, A. Margara: Taming velocity and variety simultaneously in big data with stream reasoning: tutorial. DEBS 2016: 394-401
  25. 25. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 0 Reality Capture Frame Set up a conceptual model (FraPPE) to master the variety in the data sources M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015
  26. 26. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 0 20/07/2016 Grid Cell Frame Set up a conceptual model (FraPPE) to master the variety in the data sources M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015
  27. 27. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 0 20/07/2016 Pixel Frame 1 Set up a conceptual model (FraPPE) to master the variety in the data sources M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015
  28. 28. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 0 20/07/2016 Place A Event A Set up a conceptual model (FraPPE) to master the variety in the data sources M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015
  29. 29. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 0 20/07/2016 Event A Frame 1 Set up a conceptual model (FraPPE) to master the variety in the data sources M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015
  30. 30. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015 How CitySensing works – step 0 20/07/2016 Event B Place B Frame 2 Set up a conceptual model (FraPPE) to master the variety in the data sources
  31. 31. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org  FraPPE offers an homogenous view to the visual analytics interface built on heterogeneous data How CitySensing works – step 0 31 Geo-spatial fragmentProvenance fragment Time Varying fragmentFraPPE specifics
  32. 32. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 1 32 For every pixel compute continuously the volume of Call Data Records (using privacy-preserving aggregation) Real data recorded on 13 April 2013 between 13:00 and 00:00
  33. 33. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 2 33 Find continuously the anomalous pixels comparing the current volumes with a model of the volumes in this time period Real data recorded on 13 April 2013 between 13:00 and 00:00
  34. 34. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 3 34 Map continuously anomalies to the districts of Milano Design Week Brera Tortona What's this? Real data recorded on 13 April 2013 between 13:00 and 00:00
  35. 35. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 4 35 For every anomalous pixel continuously capture the hashtags and semantic entities named in the social media streams Brera Tortona What's this? Real data recorded on 13 April 2013 between 13:00 and 00:00
  36. 36. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org How CitySensing works – step 5 36 Continuously discard the hashtags and semantic entities that are systematically used Brera Tortona Real data recorded on 13 April 2013 between 13:00 and 00:00
  37. 37. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 37 Logical architecture of CitySensing – setup time Analyse Data Stream Build Models Capture Data Stream Capture Static Data MDW
  38. 38. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 38 Logical architecture of CitySensing – run time Analyse Data Stream Build Models Detect Anomalies Capture Data Stream Visualize Analysis Store Analysis Capture Static Data MDW
  39. 39. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 39 Logical architecture of CitySensing – run time Analyse Data Stream Build Models Detect Anomalies Capture Data Stream Visualize Analysis Store Analysis Capture Static Data MDW StreamReasoning InductiveDeductive
  40. 40. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 40 Few more details on Stream Reasoning Uses logical window Connects to a variety of data streams Real-time query answering complex event processing analysis Stream Reasoner for data "in-motion" (In-memory) Store data "at-rest" (distributed) optimizes joins MDW
  41. 41. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Capturing static data via FraPPE  The frame duration was fixed to 15 minutes  Milano area was covered with • 1 grid (100x100) • 10,000 cells • 250x250 meters in each cell (the size of the mobile network cells in the centre of Milan)  During the Milano Design Week a total of 5.76 Mln pixel were captured  +1000 events in +600 places where collected using the crowd-sourced databases of fuorisalone.it, breradesigndistrict.it and tortonaroundesign.com thanks to a partnership with studiolabo 41 Cells in which there are places hosting Milan Design Week 2013 events
  42. 42. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Telecom Italia Call Data Records  1.92 Mln Gaussian models were built • one for each pixel (i.e., for each frame and cell) • grouping the frames by working and week-end days • using two months of Call Data Records, and • verifying volume of CDR has a Gaussian distribution with an Anderson-Darling test with a significance of 0.05  Built on Pig, R e Cascalog  The processing on 7 m1.large EC2 machines took 24 hours 42 Bad case Good case Histogram Histogram Q-QPlot Q-Qplot
  43. 43. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Telecom Italia Call Data Records  Volume of CDR captured in Milan during the Design Week  Calls, SMS and Internet access were aggregated (with privacy-preserving methods) and an anomaly index was computed for each of the 1.92 Mln pixel/day  The processing of 1 day on 7 m1.large EC2 took 20 mins 43 What 2013 2014 Calls 16,743,875 19,719,629 SMSs 19,454,497 20,240,485 Internet data accesses 137,381,761 197,767,245 [image: https://cerijayne.files.wordpress.com/2011/10/outliersss.png]
  44. 44. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Do CDR-anomalous pixels relate to events?  CDR-anomalous pixels =pixels in which the anomaly index is high (>+2σ and <-2σ)  To test if the anomalous pixels were related to the events of the Milan Design Week • We used three ground truth – the pixel of Milan – the pixels of Brera district – the pixels of Tortona district where there was at least an event of Milan Design Week 2013 • We compute – Precision – Recall of the anomalous pixels to find pixels in those three ground truths 44
  45. 45. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 45 Do CDR-anomalous pixels relate to events? 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 MilanBreraTorotna 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 Tuesday Wednesday Thursday Friday Saturday Sunday precision
  46. 46. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 46 Do CDR-anomalous pixels relate to events? 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 MilanBreraTorotna 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 Tuesday Wednesday Thursday Friday Saturday Sunday recall
  47. 47. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 47 Do CDR-anomalous pixels relate to events? 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 MilanBreraTorotna 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 Tuesday Wednesday Thursday Friday Saturday Sunday precision recall
  48. 48. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Social Streams  The machinery: the Streaming Linked Data framework 48 M.Balduini, E.Della Valle, D.Dell'Aglio, M.Tsytsarau, T.Palpanas, and C.Confalonieri: Social Listening of City Scale Events Using the Streaming Linked Data Framework. International Semantic Web Conference (2) 2013: 1-16 Stream Bus AnalyserDecorator Adapter Publisher VisualizerStream HTTP HTTP Data Source Streaming Linked Data Server HTML5 Browser
  49. 49. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 49 Processing Social Streams M.Balduini, A.Bozzon, E.Della Valle, Y.Huang, G-J Houben: Recommending Venues Using Continuous Predictive Social Media Analytics. IEEE Internet Computing 18(5): 28-35 (2014) Happily inside a bottle of Heineken beer @ the Heineken Magazzini #heinekendesignweek Event Milan Design Week Event Heineken Design Week Location The Magazzini hosts has location  KnowledgeGraph W Company Heineken W Drink beer produces organized by Wide as Wikipedia As deep as you like
  50. 50. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Social Streams  predictive models were built • For hastags and semantic entities systematically present • Using a Holt-Winter method • grouping the frames by – working and week-end days and – Early morning, morning, afternoon, evening, and late night • Analysing 300,000 geo-located micro-posts collected other 6 months in Milano area (november 2013, aprile 2014) • It takes few seconds per hashtag/semantic entity on a 60€/month VM in a IaaS 50 Data Fitted Forecast Lower 2,5% Upper 97,5%
  51. 51. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Social Streams  Usage of #milan in the weeks around Milan Design Week  Subtracting the predicted usage of #milan 51 200 – 700 700 – 1100 1100 – 1400 1400 – 1900 1900 – 200 200 – 700 700 – 1100 1100 – 1400 1400 – 1900 1900 – 200 WD WE WD WE WD WE WD WE WD Milan Design Week WD WE WD WE WD WE WD WE WD
  52. 52. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Social Streams  The difference between the observed and the predicted usage of #milan perfectly fits the usage of #mdw (the official hashtag of Milan Design Week) 52 200 – 700 700 – 1100 1100 – 1400 1400 – 1900 1900 – 200 200 – 700 700 – 1100 1100 – 1400 1400 – 1900 1900 – 200 WD WE WD WE WD WE WD WE WD Milan Design Week Anomalous usage of #milan Usage of #mdw
  53. 53. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Processing Social Streams  Geo-references micro-posts captured, semantically annotated, cleansed using the predictive models and analyzed in Milan area  For each pixel with at least 1 micro-post we computed  The volume related to Milano Design Week  The top-10 hashtags  The top-3 locations/events  Real-time processing was possible with our in-memory C-SPARQL engine and the Streaming Linked Data framework on a 20€/month VM in a IaaS 53 What 2013 2014 Geo-located micropost 57,154 21,782 Linked to Milano Design Week 3,569 3,499 Linked to a specific location/event 761 547
  54. 54. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Do socially active pixels relate to events?  socially active pixels =pixels in which we captured social media that talk about Milan Design Week  To computes • precision • recall of the socially active pixels in find pixels in pixels in the three ground truths about Milan, Brera district and Tortona district 54
  55. 55. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 55 Do socially active pixels relate to events? MilanBreraTorotna Tuesday Wednesday Thursday Friday Saturday Sunday precision
  56. 56. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 56 Do socially active pixels relate to events? MilanBreraTorotna Tuesday Wednesday Thursday Friday Saturday Sunday recall
  57. 57. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 57 Do socially active pixels relate to events? MilanBreraTorotna Tuesday Wednesday Thursday Friday Saturday Sunday precision recall
  58. 58. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.2 0.4 0.6 0.8 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 58 Do socially active pixels relate to events? MilanBreraTorotna Tuesday Wednesday Thursday Friday Saturday Sunday precision recall
  59. 59. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Anomalous Socially active Intersection Similar?     Are CDR-anomalous and socially active pixels similar?  Which of the following four scenarios? 59
  60. 60. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Are CDR-anomalous and socially active pixels similar?  More formally • Jaccard • E.g., 60 J(A,B) = 8/11 J(A,B) = 3/11 A B A B J(A,B) = |A ∩ B| |A∪B|
  61. 61. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0904:00 0907:00 0910:00 0913:00 0916:00 0919:00 0922:00 1001:00 1004:00 1007:00 1010:00 1013:00 1016:00 1019:00 1022:00 1101:00 1104:00 1107:00 1110:00 1113:00 1116:00 1119:00 1122:00 1201:00 1204:00 1207:00 1210:00 1213:00 1216:00 1219:00 1222:00 1301:00 1304:00 1307:00 1310:00 1313:00 1316:00 1319:00 1322:00 1401:00 1404:00 1407:00 1410:00 1413:00 1416:00 1419:00 1422:00 1501:00 61 Are CDR-anomalous and socially active pixels similar? BreraTorotna Tuesday Wednesday Thursday Friday Saturday Sunday recall CDR-anomalous recall socially active Jaccard
  62. 62. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 62 Visualizing for a casual audience
  63. 63. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 63 See it in action! http://youtu.be/MOBie09NHxM
  64. 64. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation methodology for casual audience  Guessability study • Can you guess what I mean without any explanation?  E.g. 64 Dinosaur extinction "The Shining" by Stephen King
  65. 65. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 65
  66. 66. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The patters you should have got  The CDR-anomaly and the social activity is 66 Correlated Partially correlated Not correlated
  67. 67. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 67 Q: In Brera District the volume of social media signal is partially correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  68. 68. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 68 Q: In Brera District the volume of social media signal is partially correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  69. 69. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 69 Q: In Porta Romana the volume of social media signal is strongly correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  70. 70. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 70 Q: In Porta Romana the volume of social media signal is strongly correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  71. 71. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 71 Q: In Tortona District the volume of social media signal is strongly correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  72. 72. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Evaluation of interface guessability 72 Q: In Tortona District the volume of social media signal is strongly correlated with the value of mobile anomaly signal A: 0 0.2 0.4 0.6 0.8 1
  73. 73. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Back to the research question 73 [photo: https://www.flickr.com/photos/debord/4932655275] Can we collect, analyse and repurpose • social media captured at place and events and • privacy-preserving aggregates of Call Data Records to allow visually • perceiving emerging patterns and • observing their dynamics? Yes! at least, in Milano Design Week 2013 and 2014 [photo: https://flic.kr/p/beuDaX ]
  74. 74. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org … and I was so crazy to start up a company … 74 http://www.socialometers.com
  75. 75. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Lesson Learnt for Stream Reasoning  The technical barriers are high  The theoretical foundations are incomplete  The veracity problem is sort of forgotten 75
  76. 76. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org High Technical Barriers for Stream Reasoning  We are getting close to a shared understanding on RDF Stream Processing (RDF stream and continuous extension of SPARQL) • See http://www.w3.org/community/rsp/  Missing infrastructure • Only one proposal for RDF stream publishing – http://streamreasoning.github.io/TripleWave/ • Only one proposal for RDF Stream Processing APIs – http://streamreasoning.org/resources/rsp-services  Only prototypes, some unmaintained  Need for scalable system built on Big Data technologies (e.g., Spark/Flink)  Lack of systematic and comparative evaluation • too many benchmarks all focusing RDF stream processing with little emphasis on reasoning 76
  77. 77. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org Incomplete Stream Reasoning theory  Two reference models exist • RSP-QL: Built on SPARQL semantics – D.Dell'Aglio, E. Della Valle, J-P Calbimonte, Ó. Corcho: RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of RDF Stream Processing Systems. Int. J. Semantic Web Inf. Syst. 10(4): 17-44 (2014) • LARS: Built on datalog-style rules – H.Beck, M.Dao-Tran, T.Eiter, M.Fink: LARS: A Logic-Based Framework for Analyzing Reasoning over Streams. AAAI 2015: 1431-1438  However • What's the complexity of Q/A in RSP-QL/LARS? • How to deal with inconsistency appearing over time? • How do stream reasoning and event calculus relates?  OBDA on static data ≠ OBDA for continuous querying ans = data + query Ans(t) = sys(t) + data(t) + query  What about inductive stream reasoning? 77
  78. 78. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org The veracity problem is sort of forgotten  Some initial works • D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, Y. Huang, V. Tresp, A. Rettinger, H. Wermser: Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics. IEEE Intelligent Systems 25(6): 32-41 (2010) • M. Nickles, A. Mileo: Web Stream Reasoning Using Probabilistic Answer Set Programming. RR 2014: 197- 205 • A-Y Turhan, E. Zenker: Towards Temporal Fuzzy Query Answering on Stream-based Data. HiDeSt@KI 2015: 56- 69  Missing Theory? 78
  79. 79. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 79 Take home message … guess it :-)
  80. 80. Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 80 Take home message … guess it :-) Emanuele Della Valle @manudellavalle emanuele.dellavalle@polimi.it http://emanueledellavalle.org
  81. 81. Listening to the pulse of our cities with Stream Reasoning (and few more technologies) Emanuele Della Valle @manudellavalle - emanuele.dellavalle@polimi.it http://emanueledellavalle.org

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