Un caso di studio sui big data - Campus Connestions Summit 2018 - #CCS18
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In this talk I talk about Big Data (introduction to), Microsoft Azure and its services for the Big Data. I expose and experiment of sentiment analysis on Twitter using Azure Machine Learning .
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Un caso di studio sui big data - Campus Connestions Summit 2018 - #CCS18
- 1. Campus Connections Summit 2018 Cloud computing and IT Law 4 Maggio 2018 – Pesche (Isernia) Università degli studi del Molise Dipartimento di bioscienze e territorio Fake news between law and technology 3 Maggio 2018 – Macerata Università degli studi di Macerata Dipartimento di scienze politiche, della comunicazione e delle relazioni internazionali #CCS18 SPONSORIZZATO DA
- 2. SPONSORIZZATO DA #CCS18 @angelus_giCampus Connections Summit 2018 Agenda: Introduzione al mondo dei Big Data Servizi di Microsoft Azure per i Big Data DEMO: Sentiment analysis su Twitter
- 3. SPONSORIZZATO DA #CCS18 @angelus_giCampus Connections Summit 2018 Un caso di studio sui Big Data @angelus_gi https://www.linkedin.com/in/angelusgi/ Università degli studi del Molise - 4 Maggio 2018 Angelo Gino Varrati MSP Team Leader DotNet Abruzzo cofounder
- 4. #CCS18 @angelus_giCampus Connections Summit 2018 “There were 5 exabytes of information created between the dawn of civilization through 2013, but that much information is now created every 2 days.” Eric Schmidt - Google
- 5. #CCS18 @angelus_giCampus Connections Summit 2018 Definition: «Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them.» Chellenges: «Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.»
- 6. #CCS18 @angelus_giCampus Connections Summit 2018 Da dove vengono?
- 7. #CCS18 @angelus_giCampus Connections Summit 2018 Volume Varietà Velocità Doug Laney - Teoria delle tre V
- 8. #CCS18 @angelus_giCampus Connections Summit 2018 Analisi di mercato Business Statistica La quantità non è tutto, dov’è il valore?
- 9. #CCS18 @angelus_giCampus Connections Summit 2018 Il triumvirato Cloud Big Data Intelligenza Artificiale
- 10. #CCS18 @angelus_giCampus Connections Summit 2018 Machine Learning & Big Data?
- 11. #CCS18 @angelus_giCampus Connections Summit 2018 Problemi Sicurezza Privacy
- 12. #CCS18 @angelus_giCampus Connections Summit 2018
- 13. #CCS18 @angelus_giCampus Connections Summit 2018 “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard - Gartner
- 14. #CCS18 @angelus_giCampus Connections Summit 2018 “With big data comes great responsibility”
- 15. Twitter Stream Analysis in Azure Machine Learning e PowerBI #CCS18 @angelus_giCampus Connections Summit 2018 https://bit.ly/2FrvzeM
- 16. #CCS18 @angelus_giCampus Connections Summit 2018 Number of monthly active Twitter users worldwide from 1st quarter 2010 to 4th quarter 2017 (in millions) Source https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/ +300 milioni di utenti attivi al mese su Twitter
- 17. #CCS18 @angelus_giCampus Connections Summit 2018 Microsoft Azure Data Landscape
- 18. #CCS18 @angelus_giCampus Connections Summit 2018 https://bit.ly/2HBH6xY
- 19. #CCS18 @angelus_giCampus Connections Summit 2018 https://bit.ly/2HBH6xY
- 20. #CCS18 @angelus_giCampus Connections Summit 2018 https://bit.ly/2HBH6xY
- 21. #CCS18 @angelus_giCampus Connections Summit 2018 DEMO https://bit.ly/2HBH6xY
- 22. #CCS18 @angelus_giCampus Connections Summit 2018 DEMO https://bit.ly/2FrvzeM
- 23. #CCS18 @angelus_giCampus Connections Summit 2018 https://bit.ly/2FrvzeM Sentiment Analysis con Twitter Stream Analysis in Azure Machine Learning e PowerBI su «Molise» e «UniMol» DEMO
- 25. #CCS18 @angelus_giCampus Connections Summit 2018 Q&A AngeloGino.Varrati@studentpartner.com http://angelus-gi.azurewebsites.net/ @angelus_gi https://www.linkedin.com/in/angelusgi/ Feedback https://bit.ly/2HxhGl8
Hinweis der Redaktion
- Traditional analytics tools are not well suited to capturing the full value of big data. The volume of data is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources — from back end customer databases to live web based clickstreams — are too great for any analyst to test all hypotheses and derive all the value buried in the data. Basic analytical methods used in business intelligence and enterprise reporting tools reduce to reporting sums, counts, simple averages and running SQL queries. Online analytical processing is merely a systematized extension of these basic analytics that still rely on a human to direct activities specify what should be calculated. Machine learning is ideal for exploiting the opportunities hidden in big data. t delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. Freed from the limitations of human scale thinking and analysis, machine learning is able to discover and display the patterns buried in the data.
- Analyzing data streams in real-time is a problem that a lot of businesses can relate to. In this fast paced digital era, enterprises depend on making quick and intelligent decisions. This tutorial lets you stream data from Twitter using Key Words of your choice including #hashtags and @mentions