Entity–attribute–value model

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, vertical database model and open schema. There are certain cases where an EAV schematic is an optimal approach to data modelling for a problem domain. However, in many cases where data can be modelled in statically relational terms an EAV based approach is an anti-pattern which can lead to longer development times, poor use of database resources and more complex queries when compared to a relationally-modelled data schema. Structure of an EAV table[edit] This data representation is analogous to space-efficient methods of storing a sparse matrix, where only non-empty values are stored. Data is recorded as three columns: [edit]

The EAV Data Model One problem many developers encounter while defining and analysing data requirements is the situation where a number of different attributes can be used to describe an object, but only few attributes actually apply to each one. One option is to create a table with a column representing each attribute; this is suitable for objects with a fixed number of attributes, where all or most attributes have values for a most objects. However, in our case we would end up with records where majority of columns would be empty, because attributes may be unknown or inapplicable. To solve the above problem you can apply the EAV (Entity, Attribute, Value) model. This pattern is also known under several alternative names including "object-attribute-value" model and "open schema". Sparseness of Attributes In maths and computer science, if an object only has a few attributes from a potentially large number, we call that a "sparse matrix". To illustrate this, lets look at a receipt from a supermarket.

Data Modeling 101 The goals of this article are to overview fundamental data modeling skills that all developers should have, skills that can be applied on both traditional projects that take a serial approach to agile projects that take an evolutionary approach. My personal philosophy is that every IT professional should have a basic understanding of data modeling. They don’t need to be experts at data modeling, but they should be prepared to be involved in the creation of such a model, be able to read an existing data model, understand when and when not to create a data model, and appreciate fundamental data design techniques. This article is a brief introduction to these skills. Table of Contents 1. Data modeling is the act of exploring data-oriented structures. Traditional data modeling is different from class modeling because it focuses solely on data – class models allow you to explore both the behavior and data aspects of your domain, with a data model you can only explore data issues. Figure 2.

Entity–relationship model An entity–relationship diagram using Chen's notation In software engineering, an entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements, in an abstract way that lends itself to ultimately being implemented in a database such as a relational database. The main components of ER models are entities (things) and the relationships that can exist among them. Entity-relationship modeling was developed by Peter Chen and published in a 1976 paper.[1] However, variants of the idea existed previously,[2] and have been devised subsequently such as supertype and subtype data entities[3] and commonality relationships. Overview[edit] An entity-relationship model is a systematic way of describing and defining a business process. An ER model is typically implemented as a database. The three schema approach to software engineering uses three levels of ER models that may be developed. Conceptual data model [edit]

Generic data model Example of a Generic data model.[1] Generic data models are generalizations of conventional data models. They define standardised general relation types, together with the kinds of things that may be related by such a relation type. Overview[edit] The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of whole, regardless the kind of things that are related. History[edit] Generic data models are developed as an approach to solve some shortcomings of conventional data models. Generic data model topics[edit] Generic patterns[edit] There are generic patterns that can be used to advantage for modeling business. Approach to generic data modeling[edit] Examples[edit]

gellish Gellish Modeling Method – gellish