Document - Mega-modeling for big data analytics

2012

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Mega-modeling for big data analytics

Ceri S., Della Valle E., Pedreschi D., Trasarti R.

MegaModelling Language H.2.8 Database applications Data Mining 58-02

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.

Source: Conceptual Modeling. 31st International Conference on Conceptual Modeling, pp. 1–15, Florence, Italy, 15-18 October 2012

Publisher: Springer, Berlin, DEU

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@inproceedings{oai:it.cnr:prodotti:218832,
	title = {Mega-modeling for big data analytics},
	author = {Ceri S. and Della Valle E. and Pedreschi D. and Trasarti R.},
	publisher = {Springer, Berlin, DEU},
	doi = {10.1007/978-3-642-34002-4_1},
	booktitle = {Conceptual Modeling. 31st International Conference on Conceptual Modeling, pp. 1–15, Florence, Italy, 15-18 October 2012},
	year = {2012}
}