Mega-modeling for Big Data Analytics

  • Stefano Ceri
  • Emanuele Della Valle
  • Dino Pedreschi
  • Roberto Trasarti
Conference paper

DOI: 10.1007/978-3-642-34002-4_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7532)
Cite this paper as:
Ceri S., Della Valle E., Pedreschi D., Trasarti R. (2012) Mega-modeling for Big Data Analytics. In: Atzeni P., Cheung D., Ram S. (eds) Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol 7532. Springer, Berlin, Heidelberg

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 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 analyses, predictive analytics and scenario explorations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefano Ceri
    • 1
  • Emanuele Della Valle
    • 1
  • Dino Pedreschi
    • 2
  • Roberto Trasarti
    • 3
  1. 1.DEIPolitecnico di MilanoMilanoItaly
  2. 2.Dipartimento di InformaticaUniversità di PisaPisaItaly
  3. 3.ISTI-CNR, Istituto di Scienze e Tecnologie dell’Informazione del CNRPisaItaly

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