Istituto di Scienza e Tecnologie dell'Informazione     
Ceri S., Palpanas T., Della Valle E., Pedreschi D., Freytag J., Trasarti R. Towards mega-modeling: a walk through data analysis experiences. In: ACM SIGMOD record, vol. 42 (3) pp. 19 - 27. ACM, 2013.
Big data is perceived as a fundamental ingredient for fostering the progress of science in a variety of disciplines. However, we believe that the current ICT solutions are not adequate for this challenge. Abstractions and languages for big data management are tailored to vertical domains and influenced by underlying ICT platforms, hence unsuitable for supporting "computational interdisciplinarity", as it is required if one wants to use the best of, e.g., analytical, inductive, and simulation techniques, all at work on the same data. In other words, "our society is data-rich, but it lacks the conceptual tools to handle it". In previous work, we advocate the need for a new approach to data analysis, based on mega-modeling 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. In this paper, we provide some evidence that megamodeling is a viable approach to data analysis by using a bottom-up, inductive method. We consider several experiences of data analysis research performed at our home institutions and examine them in retrospective, inducing their mega-modularization a-posteriori. This exercise convinces us that the mega-modeling approach could be highly beneficial.
URL: http://dl.acm.org/citation.cfm?id=2536673
DOI: 10.1145/2536669.2536673
Subject Language Data Mining
H.2.8 Database Applications. Data Mining

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