Next generation data integration for Life Sciences

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 domi...

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Bibliographic Details
Main Authors: Cohen-Boulakia, S, Leser, U
Format: Conference Proceeding
Language:English
Subjects:
Bioinformatics
Biology
Data models
Distributed databases
Ontologies
Semantics
Online Access:Request full text
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Summary: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 databases), the current predominantly used architecture uses materialization. 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.
ISSN:1063-6382
2375-026X
DOI:10.1109/ICDE.2011.5767957