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City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time
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City Data Fusion: A Big Data Infrastructure to sense the pulse of the city in real-time

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Streams of information flow through our cities thanks to their progressive instrumentation with diverse sensors, a wide adoption of smart phones and social networks, and a growing open release of …

Streams of information flow through our cities thanks to their progressive instrumentation with diverse sensors, a wide adoption of smart phones and social networks, and a growing open release of datasets. This research investigates the possibility to feel the pulse of our cities in real-time by fusing and making sense of all those information flows. The expected result is a Big Data infrastructure that exploits: semantic technologies, streaming databases, visual analytics, and crowd-sourcing techniques whose incentives are designed for urban environment and life styles. Early deployments for city scale events offer insights on the kind of services such infrastructure will enable.

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  • 1. City Data Fusion http://citydatafusion.org A Big Data Infrastructure to sense the pulse of the city in real-time Emanuele Della Valle emanuele.dellavalle@polimi.it http://emanueledellavalle.org IBM and Politecnico di Milano bridging industrial and academic excellence 2.10.2013
  • 2. http://citydatafusion.org - Emanuele Della Valle Agenda  Context  Goal  An example  Proposed solution  Research hypothesis  Early deployments  On-going collaborations 3
  • 3. http://citydatafusion.org - Emanuele Della Valle The digital reflection of reality is sharpening Streams of information flows through our cities thanks to: 4 the pervasive deployment of sensors in our cities the wide adoption of smart phones (equipped with sensors) the usage of (location-based) social networks the availability of datasets about urban environment
  • 4. http://citydatafusion.org - Emanuele Della Valle Goal  Advance our ability to feel the pulse of our cities in order to deliver innovative services 5 fusing all those data sources making sense of the fused information
  • 5. http://citydatafusion.org - Emanuele Della Valle E.g., is Milano Design Week perceivable? 6 Step 1: associate mobile traffic to urban areas Real data recorded on 13 April 2013 between 13:00 and 00:00
  • 6. http://citydatafusion.org - Emanuele Della Valle E.g., is Milano Design Week perceivable? 7 Step 2: subtract what is systematic Real data recorded on 13 April 2013 between 13:00 and 00:00
  • 7. http://citydatafusion.org - Emanuele Della Valle E.g., is Milano Design Week perceivable? 8 Step 3: Identify interesting areas Brera Navigli Porta Romana Real data recorded on 13 April 2013 between 13:00 and 00:00
  • 8. http://citydatafusion.org - Emanuele Della Valle E.g., is Milano Design Week perceivable? 9 Step 4: retrieve the top hashtags Brera Navigli Porta Romana Real data recorded on 13 April 2013 between 13:00 and 00:00
  • 9. http://citydatafusion.org - Emanuele Della Valle E.g., is Milano Design Week perceivable? 10 Step 5: exclude what is systematic Brera Navigli Porta Romana Real data recorded on 13 April 2013 between 13:00 and 00:00
  • 10. http://citydatafusion.org - Emanuele Della Valle Ingredients of the proposed Big Data infrastructure  semantic technologies - Address "variety" using Ontology Based Data Access - Named Entity recognition and linkage - Knowledge discovery (e.g., detecting systematicy)  streaming algorithms - Address "velocity" of data stream - Address "volume" by being able to process data that do not fit in main memory  crowd-sourcing techniques - Address "veracity" by cleansing and enriching data  Visual analytics - Allow no-expert access to data - Tell stories out of data 11
  • 11. http://citydatafusion.org - Emanuele Della Valle ? ? ? ?? Limitation of current systems  Insufficient methods for making sense in real- time of heterogeneous data and social streams w.r.t. the vast collections of (open) data  Lack of crowd-sourcing techniques whose incentives leverage needs of people in the urban environment  Lack of visualization techniques tailored to non-experts 12
  • 12. http://citydatafusion.org - Emanuele Della Valle Research hypothesis 1. To scale order matters 2. Crowdsourcing needs the urban-centric incentives 3. Visualization must tell stories 13
  • 13. http://citydatafusion.org - Emanuele Della Valle Research hypothesis: order matters!  Observation: order reflects recency, relevance, trustability …  harnessing orders is key to make sense in real-time of heterogeneous, massive and volatile data 14 Indexes Recency Relevance, Trustability, etc. Combinations Typesoforders No Yes Traditional solutions DSMS/CEP Top-k Q/A Continuous top-k Q/A Scalable reasoning Stream reasoning Order-aware reasoning Top-k Reasoning Types of reasoning
  • 14. http://citydatafusion.org - Emanuele Della Valle Research hypothesis: urban-centric incentives!  incentives designed for urban environment and life styles are key 15 Maslow'shierarchyofneeds [source: http://www.behance.net/gallery/Maslows-Hierarchy-of-Needs-Infographic/4376921 ]
  • 15. http://citydatafusion.org - Emanuele Della Valle Research hypothesis: visualizations must tell stories 16 [Source: http://www.densitydesign.org/2013/04/whatever-the-weather/ ]
  • 16. http://citydatafusion.org - Emanuele Della Valle Prototypes already deployed 17 http://streamreasoning.org/demos/bottari http://streamreasoning.org/demos/london2012 http://twindex.fuorisalone.it/
  • 17. http://citydatafusion.org - Emanuele Della Valle On-going collaborations Who Semantic techs Streaming algorithm s Crowd- sourcing Visual analytics CEFRIEL Density Design Lab – PoliMi DISI – University of Trento KDD Lab – ISTI, CNR, Pisa ML Group – SIEMENS Ontology Eng. Group – UPM Saltlux – Korea SKIL Lab - Telecom Italia Studio Labo Web IS - TU Delft 18
  • 18. City Data Fusion http://citydatafusion.org Thank you! Emanuele Della Valle emanuele.dellavalle@polimi.it http://emanueledellavalle.org IBM and Politecnico di Milano bridging industrial and academic excellence 2.10.2013

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