PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M. How data mining and machine learning evolved from relational data base to data science. Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Sacc (eds.). (Studies in Big Data, vol. 31). [Online First 31 May 2017] Cham, Switzerland: Springer, 2018.
 
 
Abstract
(English)
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.
URL: http://https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17
DOI: 10.1007/978-3-319-61893-7_17
Subject Data mining
Sentiment analysis
Text classification
Image classification
Trajectory mining
H.2.8 DATABASE MANAGEMENT. Database Applications. Data mining
H.2.8 DATABASE MANAGEMENT. Database Applications. Image databases
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval


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