%0 Journal Article
%T A perspective for biomedical data integration: Design of databases for flow cytometry
%A John Drakos
%A Marina Karakantza
%A Nicholas C Zoumbos
%A John Lakoumentas
%A George C Nikiforidis
%A George C Sakellaropoulos
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-99
%X The proposed database schema provides integration of data originating from diverse acquisition settings, organized in a way that allows syntactically simple queries that provide results significantly faster than the conventional implementations of the FCS standard. The proposed schema can potentially achieve up to 8 orders of magnitude reduction in query complexity and up to 2 orders of magnitude reduction in response time for data originating from flow cytometers that record 256 colours. This is mainly achieved by managing to maintain an almost constant number of data-mining procedures regardless of the size and complexity of the stored information.It is evident that using single-file data storage standards for the design of databases without any structural transformations significantly limits the flexibility of databases. Analysis of the requirements of a specific domain for integration and massive data processing can provide the necessary schema modifications that will unlock the additional functionality of a relational database.The integration of biomedical information has become an essential task for health care professionals. Current progress in the domain of Information Technology allows for huge data storages and powerful computational possibilities to be affordable; thus, they have been quite common. Researchers are gradually becoming aware of the importance of keeping together diverse data pertaining to a specific medical entity and successful attempts to create and maintain such databases are becoming known to the scientific community [1].Data models specified by standards are often included in databases, without taking into account inherent limitations posed by the procedure of acquiring original data. It is therefore quite often that inference is affected by errors that propagate throughout the entire process, from data acquisition through processing and analysis. While data models are adequate for their initial usage, they are inflexible to the require
%U http://www.biomedcentral.com/1471-2105/9/99