attributecentric

Attributecentric is a design and analysis paradigm that centers on attributes—properties or characteristics—of entities rather than on identity, relationships, or processes. The term is used across fields to describe methods that organize, reason about, and manipulate information primarily through attribute-value descriptions.

In data modeling, an attributecentric model represents objects mainly by their attribute sets. Entities are described by their fields, with schemas that emphasize attribute definitions, data types, constraints, and nullable options. This contrasts with object- or relation-centric approaches that foreground identity or inter-entity connections.

In knowledge representation and databases, attributecentric representations rely on structured attribute-value schemas, enabling flexible schema evolution and straightforward querying of criteria expressed over attributes. In machine learning, the related idea is a feature-centric approach that focuses on observable characteristics used for prediction rather than the broader object structure.

In software design, attributecentric interfaces and data transfer objects highlight fields and validation rules, supporting modular components and clear data contracts. This orientation can simplify filtering, indexing, and interoperability when attributes are the primary basis for decisions.

In practice, choosing an attributecentric approach depends on the domain: where attributes drive queries and decisions, and where relationships are secondary. It is commonly contrasted with entity-centric or relationship-centric design, which prioritize object identity and connections.