Cluster Based Integration of Heterogeneous Biological Databases Using the AutoMed Toolkit | Zendy

Michael Maibaum | Zendy; Lucas Zamboulis | Zendy; Galia Rimon | Zendy; Christine Orengo | Zendy; Nigel Martin | Zendy; Alexandra Poulovassilis | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Cluster Based Integration of Heterogeneous Biological Databases Using the AutoMed Toolkit

Author(s) -

Michael Maibaum,

Lucas Zamboulis,

Galia Rimon,

Christine Orengo,

Nigel Martin,

Alexandra Poulovassilis

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-27967-9

DOI - 10.1007/11530084_16

Subject(s) - data warehouse , computer science , data integration , cluster analysis , architecture , database , biological database , biological data , extensibility , data mining , identification (biology) , data architecture , data transformation , reference architecture , software architecture , artificial intelligence , programming language , bioinformatics , software , art , botany , visual arts , biology

This paper presents an extensible architecture that can be used to support the integration of heterogeneous biological data sets. In our architecture, a clustering approach has been developed to support distributed biological data sources with inconsistent identification of biological objects. The architecture uses the AutoMed data integration toolkit to store the schemas of the data sources and the semi-automatically generated transformations from the source data into the data of an integrated warehouse. AutoMed supports bi-directional, extensible transformations which can be used to update the warehouse data as entities change, are added, or are deleted in the data sources. The transformations can also be used to support the addition or removal of entire data sources, or evolutions in the schemas of the data sources or of the warehouse itself. The results of using the architecture for the integration of existing genomic data sets are discussed

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research