Biton, Olivier

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  • Publication
    Orchestra: Facilitating Collaborative Data Sharing
    (2007-06-11) Green, Todd J; Karvounarakis, Grigoris; Taylor, Nicholas E; Biton, Olivier; Ives, Zachary G; Tannen, Val
    One of the most elusive goals of structured data management has been sharing among large, heterogeneous populations: while data integration [4, 10] and exchange [3] are gradually being adopted by corporations or small confederations, little progress has been made in integrating broader communities. Yet the need for large-scale sharing of heterogeneous data is increasing: most of the sciences, particularly biology and astronomy, have become data-driven as they have attempted to tackle larger questions. The field of bioinformatics, in particular, has seen a plethora of different databases emerge: each is focused on a related but subtly different collection of organisms (e.g., CryptoDB, TIGR, FlyNome), genes (GenBank, GeneDB), proteins (UniProt, RCSB Protein Databank), diseases (OMIM, GeneDis), and so on. Such communities have a pressing need to interlink their heterogeneous databases in order to facilitate scientific discovery.