Anatomy of data integration.
Producing reliable information is the ultimate goal of data processing. The ocean of data created with the advances of science and technologies calls for integration of data coming from heterogeneous sources that are diverse in their purposes, business rules, underlying models and enabling technologies. Reference models, Semantic Web, standards, ontology, and other technologies enable fast and efficient merging of heterogeneous data, while the reliability of produced information is largely defined by how well the data represent the reality. In this paper, we initiate a framework for assessing the informational value of data that includes data dimensions; aligning data quality with business practices; identifying authoritative sources and integration keys; merging models; uniting updates of varying frequency and overlapping or gapped data sets.
Brazhnik
Olga
O
Center for Information Technology, National Institutes of Health, 10401 Fernwood Road, Room 3NW03, Bethesda, MD 20817, USA. brazhnik@nih.gov
Jones
John F
JF
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval; D020407:Internet; D008490:Medical Informatics / Q000379:methods*; D016347:Medical Records Systems, Computerized; D008962:Models, Theoretical; D012984:Software; D018511:Systems Integration