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Next Generation Data Integration for the Life Sciences

Sarah Cohen-Boulakia 1, 2 Ulf Leser 3
2 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
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.
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Contributor : Sarah Cohen-Boulakia <>
Submitted on : Thursday, December 2, 2010 - 1:56:31 PM
Last modification on : Wednesday, September 16, 2020 - 5:05:42 PM


  • HAL Id : inria-00542359, version 1



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



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