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The concept-oriented data model is a data model based on lattice theory and ordered sets. Another source of inspiration in creating this model is formal concept analysis (FCA). more...

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One of the main ideas underlying the concept-oriented approach is that the model has to be hierarchical and multidimensional (simultaneously). It also intersects with the Functional Data Model (FDM) and the Universal Relation Model (URM).

The fundamental principle of the concept-oriented paradigm is that objects are living in space where the space structure describes the model syntax or schema while the object structure represents its semantics. Elementary parts of the space are referred to as concepts while objects, which are concept instances are called data items. Concepts are analogous to relations or tables while items are analogous to rows or records in the relational model.

The concept-oriented model allows the database designer to describe a natural representation of data syntax and semantics, which reflects both hierarchical and multidimensional properties. Proceeding from only a small number of basic notions and principles this approach allows modeling a variety of existing methods and practical use cases such as multi-valued variables, higher level relationships, grouping and aggregation, online analytical processing (OLAP), inference, lifecycle management, complex categorization, ontologies, knowledge sharing and many other mechanisms.

Model syntax

At the syntactic level each concept is defined as a combination of its superconcepts. As a consequence a subconcept is included into each of its superconcepts simultaneously. Formally the model syntax or schema are complemented by one top concept and one bottom concepts and this structure then constitutes a lattice. The top concept is a direct or indirect parent for any other concept in the model while the bottom concept directly or indirectly includes any other concept in the model.

Alternatively the concept-oriented syntax (schema) can be described in the conventional terms of dimensions and domains. Each superconcept in the definition of the concept is supposed to be a domain for the dimension associated with this pair of subconcept-superconcept. A dimension normally has a unique name within the scope of its concept. Thus each concept is defined a set of dimension names with their domains in other concepts. The database schema can be then represented as an acyclic graph where nodes are concepts and edges are dimensions leading from a concept to its domains in superconcepts. Dual to dimension is the notion of inverse dimension, which is thought of as a characteristic or attribute taking values from some subconcept (rather than superconcept). It is important that dimensions are single valued while inverse dimensions are multi-valued.

Read more at Wikipedia.org