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Big Data and Stream Data Analysis at Politecnico di Milano

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Problems, Techniques, Scenarios and Partners.
Covering social media, IoT, smart city and many more cases.
By Marco Brambilla and Emanuele Della Valle.

Publié dans : Médias sociaux
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Big Data and Stream Data Analysis at Politecnico di Milano

  1. 1. Big Data Analysis Marco Brambilla, Emanuele Della Valle @marcobrambi @manudellavalleData Science Lab
  2. 2. Who we are • Marco Brambilla • Associate Professor at Computer Science Dept. (DEIB) • Expert in data science, social media analysis, conceptual modeling • Inventor of IFML international standard for user interaction with data • Emanuele Della Valle • Assistant Professor at Computer Science Dept. (DEIB) • Expert in semantic technologies and stream computing • Brander of stream reasoning, tackling velocity and variety of Big Data • 15 years experience in research and industrial projects • Startuppers: fluxedo.com, webratio.com • R&D advisors: socialometers.com, semioty.com, ifml.org 2
  3. 3. [source: IBM, 2012] Big Data dimensions Marco Brambilla and Emanuele Della Valle - Big Data Analysis
  4. 4. Our Activities Full coverage of the Big Data Processing pipeline Marco Brambilla and Emanuele Della Valle - Big Data Analysis Data Acquisition Data Preparation Data Analysis Reporting Prediction Action
  5. 5. Data Fusion Scenario Marco Brambilla and Emanuele Della Valle - Big Data Analysis
  6. 6. Data Fusion Scenario
  7. 7. Data Fusion Scenario • Social Media Data • Opinion mining • Telecommunications Data • WI-FI Log • IoT • 3D Cameras • Any other IoT enabled sensors • Possibly embedded in industrial productsMarco Brambilla and Emanuele Della Valle - Big Data Analysis
  8. 8. Como City Monitoring Marco Brambilla and Emanuele Della Valle - Big Data Analysis
  9. 9. Como City Monitoring
  10. 10. … and it’s just the beginning • CRM + IoT • User Engagement • Recommendation • Resource management • Predictive maintenance Company Object Customer Marco Brambilla and Emanuele Della Valle - Big Data Analysis
  11. 11. Stream Reasoning research Tame Variety and Velocity simultaneously Marco Brambilla and Emanuele Della Valle - Big Data Analysis TRADITIONAL APPROACH Data “in-motion” Data “in-motion” Registered analysis Insights “in-motion” Data put “at-rest” in DWH Analysis Analysis Insight PANOPTIQUE APPROACH Ontology + Mappings Traditional approach StreamReasoning
  12. 12. Paradigmatic change enabled by Stream Reasoning Tame Variety and Velocity simultaneously Marco Brambilla and Emanuele Della Valle - Big Data Analysis TRADITIONAL APPROACH Data “in-motion” Data “in-motion” Registered analysis Insights “in-motion” Data put “at-rest” in DWH Analysis Analysis Insight PANOPTIQUE APPROACH Ontology + Mappings Traditional approach StreamReasoning
  13. 13. Our Stream Reasoner Uses logical window Connects to a variety of data streams Real-time predictive and actionable insights Stream Reasoner for data "in-motion" (In-memory) Store data "at-rest" (distributed) post-hoc analysis optimizes joins
  14. 14. Usages of our Research Temporal, spatial, statistical analysis Predictive analysis Real-time and post-hoc data collection, fusion & cleansing to enable as actionable insights and visual data exploration Fine grained information access Complex event processing
  15. 15. – Off-shore oil operations: – Smart Cities – Global Contact Center – Social networks At work with our partners Marco Brambilla and Emanuele Della Valle - Big Data Analysis
  16. 16. At work with our partners Marco Brambilla and Emanuele Della Valle - Big Data Analysis – Events: Fashion and Design – Utilities, Resources, Wholesale – Sharing Economy
  17. 17. Contacts emanuele.dellavalle@polimi.it @manudellavalle http://emanueledellavalle.org marco.brambilla@polimi.it @marcobrambi http://about.me/brambillamarco

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