Entity-Attribute-Value (EAV) Data Model

From GM-RKB
(Redirected from EAV Data Model)
Jump to navigation Jump to search

An Entity-Attribute-Value (EAV) Data Model is a data model that represents each Entity's Attribute Value as a Data Record.



References

2011

Entity-attribute-value model (EAV) is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. In mathematics, this model is known as a sparse matrix. EAV is also known as object-attribute-value model and open schema.

Structure of an EAV table

This data representation is analogous to space-efficient methods of storing a sparse matrix, where only non-empty values are stored. In an EAV data model, each attribute-value pair is a fact describing an entity, and a row in an EAV table stores a single fact. EAV tables are often described as "long and skinny": "long" refers to the number of rows, "skinny" to the few columns.

Data is recorded as three columns:

  • The entity: the item being described.
  • The attribute or parameter: a foreign key into a table of attribute definitions. At the very least, the attribute definitions table would contain the following columns: an attribute ID, attribute name, description, data type, and columns assisting input validation, e.g., maximum string length and regular expression, set of permissible values, etc.
  • The value of the attribute.

2008

1999

  • (Nadkarni et al., 1999) ⇒ P.M. Nadkarni, L. Marenco, R. Chen, E. Skoufos, G. Shepherd, and P. Miller. (1999). “Organization of Heterogeneous Scientific Data Using the EAV/CR Representation.” In: Journal of the American Medical Informatics Association, 6(6).
    • ABSTRACT: Entity-attribute-value (EAV) representation is a means of organizing highly heterogeneous data using a relatively simple physical database schema. EAV representation is widely used in the medical domain, most notably in the storage of data related to clinical patient records. Its potential strengths suggest its use in other biomedical areas, in particular research databases whose schemas are complex as well as constantly changing to reflect evolving knowledge in rapidly advancing scientific domains. When deployed for such purposes, the basic EAV representation needs to be augmented significantly to handle the modeling of complex objects (classes) as well as to manage interobject relationships. The authors refer to their modification of the basic EAV paradigm as EAV/CR (EAV with classes and relationships). They describe EAV/CR representation with examples from two biomedical databases that use it.