The objective of the module is to align the competences of the students in computer science and base, in particular about databases, structured data analysis and programming languages.
The aim of this course is to introduce the student with the high performance Big Data management tools. The student will gain expertise in the use od NO-SQL platforms for the analysis and mining of large data volumes, thus performing tasks that would not be feasible with traditional data bases.
The aim of this course is to introduce the student with the high performance Big Data management tools. The student will gain expertise in the use od NO-SQL platforms for the analysis and mining of large data volumes, thus performing tasks that would not be feasible with traditional data bases.
This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.
This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.
This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.
This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.
This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.
This module presents how to analyse traces that users leave from querying Web search engines (query log). It presents the main applications of Web mining including: i) how to profile the interests/activities of users, ii) how to use information from query logs for forecasting social indicators and optimizing Web search engines. Teaching activities will be supported by several case studies developed in the SoBigData.eu laboratory.
This module presents how to analyse traces that users leave from querying Web search engines (query log). It presents the main applications of Web mining including: i) how to profile the interests/activities of users, ii) how to use information from query logs for forecasting social indicators and optimizing Web search engines. Teaching activities will be supported by several case studies developed in the SoBigData.eu laboratory.
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