Next Generation Data Integration for the Life Sciences

Sarah Cohen-Boulakia 1, 2 Ulf Leser 3
2 AMIB - Algorithms and Models for Integrative Biology
CNRS - Centre National de la Recherche Scientifique : UMR8623, Polytechnique - X, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
Abstract : Ever since the advent of high-throughput biology (e.g., the Human Genome Project), integrating the large number of diverse biological data sets has been considered as one of the most important tasks for advancement in the biological sciences. Whereas the early days of research in this area were dominated by virtual integration systems (such as multi-/federated databas-es), the current predominantly used architecture uses materiali-zation. Systems are built using ad-hoc techniques and a large amount of scripting. However, recent years have seen a shift in the understanding of what a “data integration system” actually should do, revitalizing research in this direction. In this tutorial, we review the past and current state of data integration for the Life Sciences and discuss recent trends in detail, which all pose challenges for the database community.
Type de document :
Communication dans un congrès
IEEE International Conference on Data Engineering (ICDE), Apr 2011, Hannover, Germany. 2011
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https://hal.inria.fr/inria-00542359
Contributeur : Sarah Cohen-Boulakia <>
Soumis le : jeudi 2 décembre 2010 - 13:56:31
Dernière modification le : jeudi 11 janvier 2018 - 06:23:08

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  • HAL Id : inria-00542359, version 1

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Sarah Cohen-Boulakia, Ulf Leser. Next Generation Data Integration for the Life Sciences. IEEE International Conference on Data Engineering (ICDE), Apr 2011, Hannover, Germany. 2011. 〈inria-00542359〉

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