PubMed · 16259140
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Mike McBride. 2005. An ideal dics storage solution.. https://pubmed.ncbi.nlm.nih.gov/16259140/
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PURPOSE: To introduce the goals of EAV database modeling, to describe the situations where entity-attribute-value (EAV) modeling is a useful alternative to conventional relational methods of database modeling, and to describe the fine points of implementation in production systems. METHODS: We analyze the following circumstances: (1) data are sparse and have a large number of applicable attributes, but only a small fraction will apply to a given entity; (2) numerous classes of data need to be represented, each class has a limited number of attributes, but the number of instances of each class is very small. We also consider situations calling for a mixed approach where both conventional and EAV design are used for appropriate data classes. RESULTS AND CONCLUSIONS: In robust production systems, EAV-modeled databases trade a modest data sub-schema for a complex metadata sub-schema. The need to design the metadata effectively makes EAV design potentially more challenging than conventional design.
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MOTIVATION: Annotation databases are widely used as public repositories of biological knowledge. However, most of these resources have been developed by independent groups which used different designs and different identifiers for the same biological entities. As we show in this article, incoherent name spaces between various databases represent a serious impediment to using the existing annotations at their full potential. Navigating between various such name spaces by mapping IDs from one database to another is a very important issue which is not properly addressed at the moment. RESULTS: We have developed a web-based resource, Onto-Translate (OT), which effectively addresses this problem. OT is able to map onto each other different types of biological entities from the following annotation databases: Swiss-Prot, TrEMBL, NREF, PIR, Gene Ontology, KEGG, Entrez Gene, GenBank, GenPept, IMAGE, RefSeq, UniGene, OMIM, PDB, Eukaryotic Promoter Database, HUGO Gene Nomenclature Committee and NetAffx. Currently, OT is able to perform 462 types of mappings between 29 different types of IDs from 17 databases concerning 53 organisms. Among these, over 300 types of translations and 15 types of IDs are not currently supported by any other tool or resource. On average, OT is able to correctly map between 96 and 99% of the biological entities provided as input. In terms of speed, sets of approximately 20 000 IDs can be translated in <30 s, in most cases. AVAILABILITY: OT is a part of Onto-Tools, which is freely available at http://vortex.cs.wayne.edu/Projects.html