Data warehouses architecture

Concordance. A data warehouse architecture used in MDL relational chemical and reaction databases. The central "fact" table of a. concordance has a record for each unique structure in the database, with pointers to the instances of the structure in various "source" databases. 

Star Schema. A standard data warehouse architecture, characterized by Ralph Kimball, in which a central "fact" table is connected to various "dimension" tables. 

A data warehouse architecture is illustrated in Fig. 20 (Anahory and Murray, 1997). A data warehouse contains three principal categories of information detailed information (for example, in the genomic context, this may be the raw sequence data or other data on the function of individual protein) summary information (for example, in the genome context, this may record the results of aggregations such as the numbers of genes associated with a given function in each species) and meta data (which describes the information in the warehouse and where it came from). 

The primary result of the DWQ project consists of a neutral architectural reference model covering the design, the operation, the maintenance, and the evolution of data warehouses. The architecture model is based on the idea, that any data warehouse component can be seen from three different perspectives The conceptual perspective, the logical perspective, and the physical perspective. In the design, operation, and especially evolution of data warehouses, it is crucial that these three perspectives are maintained consistent with each other. 

Data warehouses have established themselves in the information flow architectures of business organizations for two main reasons firstly, as a buffer between operational and transactional tasks on the one hand, and analytical strategic tasks on the other secondly, to capture the history of business transactions for the purpose of archiving, traceability, experience mining, and reuse. 

The Cancer Bioinformatics Infrastructure Objects (caBIO) [16] (http //ncicb.nci.nih. gov/core/caBIO) is the primary architecture for data integration at the National Cancer Institute (NCI). It is based on a data warehouse that is used to integrate several of the NCI data sources. The content of the caBIO data warehouse is refreshed approximately biweekly and consists of variety of National Institutes of Health datasets, including genomic, expression, pathway, and clinical trials data. 

Key Words High-performance computing pharmacogenomic information infrastructure database database management system software architecture hardware architecture web service data warehouse federated database system parallel processing data storage. 

Transactional systems serve to automate the many transactions that are part of health care delivery. These alone are not sufficient to realize the full potential of information technology, and any comprehensive plan should include an analytical system that allows detailed analysis and queries across patient longitudinal records for the purposes of quality improvement, evaluation of patient outcomes, and other research. Such systems are usually classified as data warehouses and are founded on architectures that facilitate analysis and activities such as data mining in response b> ad hoc queries. 

Integration of multivendor software systems, software maintenance (laboratory technicians), open source, code and data warehouses, component architectures, interoperability, security top two issues component architectures and open source code... 

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