%0 Book Section %1 ceri2012megamodeling %A Ceri, Stefano %A Valle, Emanuele %A Pedreschi, Dino %A Trasarti, Roberto %B Conceptual Modeling %D 2012 %E Atzeni, Paolo %E Cheung, David %E Ram, Sudha %I Springer Berlin Heidelberg %K conceptual_modeling er toorganize %P 1-15 %R 10.1007/978-3-642-34002-4_1 %T Mega-modeling for Big Data Analytics %U http://dx.doi.org/10.1007/978-3-642-34002-4_1 %V 7532 %X The availability of huge amounts of data (“big data”) is changing our attitude towards science, which is moving from specialized to massive experiments and from very focused to very broad research questions. Models of all kinds, from analytic to numeric, from exact to stochastic, from simulative to predictive, from behavioral to ontological, from patterns to laws, enable massive data analysis and mining, often in real time. Scientific discovery in most cases stems from complex pipelines of data analysis and data mining methods on top of “big” experimental data, confronted and contrasted with state-of-art knowledge. In this setting, we propose %@ 978-3-642-34001-7

@incollection{ceri2012megamodeling, abstract = {The availability of huge amounts of data (“big data”) is changing our attitude towards science, which is moving from specialized to massive experiments and from very focused to very broad research questions. Models of all kinds, from analytic to numeric, from exact to stochastic, from simulative to predictive, from behavioral to ontological, from patterns to laws, enable massive data analysis and mining, often in real time. Scientific discovery in most cases stems from complex pipelines of data analysis and data mining methods on top of “big” experimental data, confronted and contrasted with state-of-art knowledge. In this setting, we propose }, added-at = {2012-12-17T14:31:39.000+0100}, author = {Ceri, Stefano and Valle, Emanuele and Pedreschi, Dino and Trasarti, Roberto}, biburl = {https://www.bibsonomy.org/bibtex/28d963042df134f622519950d949cc221/schmidt2}, booktitle = {Conceptual Modeling}, description = {Mega-modeling for Big Data Analytics - Springer}, doi = {10.1007/978-3-642-34002-4_1}, editor = {Atzeni, Paolo and Cheung, David and Ram, Sudha}, interhash = {01b7c4ffcd266ed3e88bfdb4cb64bc34}, intrahash = {8d963042df134f622519950d949cc221}, isbn = {978-3-642-34001-7}, keywords = {conceptual_modeling er toorganize}, pages = {1-15}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2012-12-17T14:31:39.000+0100}, title = {Mega-modeling for Big Data Analytics}, url = {http://dx.doi.org/10.1007/978-3-642-34002-4_1}, volume = 7532, year = 2012 }