Istituto di Scienza e Tecnologie dell'Informazione     
Ceri S., Della Valle E., Pedreschi D., Trasarti R. Mega-modeling for big data analytics. In: ER 2012 - Conceptual Modeling. 31st International Conference on Conceptual Modeling (Florence, Italy, 15-18 October 2012). Proceedings, pp. 1 - 15. Atzeni Paolo, Cheung David, Ram Sudha (eds.). (Lecture Notes in Computer Science, vol. 7532). Springer, 2012.
The availability of huge amounts of data ("big data") is changing our attitude towards science, which is moving from specialized to massive experi- ments 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 mas- sive 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 mega-modelling as a new holistic data and model management system for the acquisition, composition, integration, management, querying and mining of data and models, capable of mastering the co-evolution of data and models and of supporting the creation of what-if anal- yses, predictive analytics and scenario explorations.
URL: http://link.springer.com/chapter/10.1007/978-3-642-34002-4_1
DOI: 10.1007/978-3-642-34002-4_1
Subject MegaModelling
Data Mining
H.2.8 Database applications. Data mining

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