Web Science. An introduction
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The document introduces web science, highlighting the significance of big data in contemporary society and the challenges it presents, such as data volume, variety, velocity, and veracity. It outlines the importance of data analytics, knowledge extraction, and various technologies and methods, including semantic web and data wrangling. The agenda includes discussions on classification, recommender systems, and case studies, emphasizing the relevance of these topics for future success.
Web Science. An introduction
- 1. Web Science Intro Marco Brambilla, Emanuele Della Valle @marcobrambi @manudellavalleWeb Science
- 2. There are more things In heaven and earth, Horatio, Than are dreamt of in your philosophy. Shakespeare (Hamlet Act 1, scene 5)
- 3. The data deluge: social and pervasive data
- 4. 1 GigaByte of data
- 5. 1 ZettaByte of data (*) Or 152 million years of high-definition video.
- 6. The new data revolution: From business to society
- 7. What’s out in the world The current IT revolution (Google, Amazon, Uber, Airbnb) focuses on large scale data management & analysis
- 8. Climate, energy, transportation & smart cities, disaster recovery, personalized medicine & health. Data can change the society! Big data challenges for mankind
- 9. From Data to Wisdom
- 10. Data: models, languages, user interaction, analysis, mining and optimization.
- 11. Formalizing new knowledge is hard Only high frequency emerges The long tail challenge
- 12. Knowledge Extraction Text mining Semantic Web Multimedia data extraction …
- 13. Heaven and Heart How to peer through an effective window on real world? Social media, our blessing and curse Domain experts matter
- 14. Beware the streetlamp effect The bias of the source The bias of the observer
- 16. Conclusion Learning about big data, data analytics and web data extraction is prominent for your future success!
- 17. Agenda Web Data API Semantic Web OWL RDF+Web Data Publishing SPARQL + R2RML invited class - Harvard Data Wrangling R language
- 18. Agenda Classification + Clustering Recommender Systems + Diversification Graph Analysis Big Data Technology Streaming Data and API Spark Human Computation Crowdsourcing Case studies and conclusion
- 19. Evaluation (aka., exam) • Written exam with theory + exercises • Project work (in 3-persons groups) with discussion • Optional: reading and presentation
- 20. Logistics • Calendar always updated online https://docs.google.com/spreadsheets/d/1YRXWK5ihby0hr8u6azkTKrWjylFgaCtoFGUsgElYoms/edit?usp=sharing • Web site of the course with all materials
- 21. Marco Brambilla, @marcobrambi, marco.brambilla@polimi.it Marco Brambilla, @manudellavalle, emanuele.dellavalle@polimi.it http://datascience.deib.polimi.it