Resources, Events, Agents

Last updated

Resources, events, agents (REA) is a model of how an accounting system can be re-engineered for the computer age. REA was originally proposed in 1982 by William E. McCarthy as a generalized accounting model, and contained the concepts of resources, events and agents (McCarthy 1982).

REA is a popular model in teaching accounting information systems (AIS), but it is rare in business practice. Most companies cannot easily dismantle their legacy data warehouse systems or are unwilling to do so. IBM Scalable Architecture for Financial Reporting, REATechnology, and ISO 15944-4 are exceptions. Fallon and Polovina (2013) have however shown how REA can also add value when modelling current ERP business processes by providing a tool which increases the understanding of the implementation and underlying data model.

Description

The REA model gets rid of many accounting objects that are not necessary in the computer age. Most visible of these are debits and credits—double-entry bookkeeping disappears in an REA system. Many general ledger accounts also disappear, at least as persistent objects; e.g., accounts receivable or accounts payable. The computer can generate these accounts in real time using source document records.

REA treats the accounting system as a virtual representation of the actual business. In other words, it creates computer objects that directly represent real-world-business objects. In computer science terms, REA is an ontology. The real objects included in the REA model are:

These objects contrast with conventional accounting terms such as asset or liability, which are less directly tied to real-world objects. For example, a conventional accounting asset such as goodwill is not an REA resource.

There is a separate REA model for each business process in the company. A business process roughly corresponds to a functional department, or a function in Michael Porter's value chain. Examples of business processes would be sales, purchases, conversion or manufacturing, human resources, and financing.

At the heart of each REA model there is usually a pair of events, linked by an exchange relationship, typically referred to as the "duality" relation. One of these events usually represents a resource being given away or lost, while the other represents a resource being received or gained. For example, in the sales process, one event would be "sales"—where goods are given up—and the other would be "cash receipt", where cash is received. These two events are linked: a cash receipt occurs in exchange for a sale, and vice versa. The duality relationship can be more complex, e.g., in the manufacturing process, it would often involve more than two events (see Dunn et al. [2004] for examples).

REA systems have usually been modeled as relational databases with entity-relationship diagrams, though this is not compulsory.

The philosophy of REA draws on the idea of reusable Design Patterns, though REA patterns are used to describe databases rather than object-oriented programs, and are quite different from the 23 canonical patterns in the original designs pattern book by Gamma et al. Research in REA emphasizes patterns (e.g., Hruby et al. 2006). Here is an example of the basic REA pattern shown as an E-R diagram:

ReaExample.png

The pattern is extended to encompass commitments (promises to engage in transactions, e.g., a sales order), policies, and other constructs. Dunn et al. (2004) provide a good overview at an undergraduate level (for accounting majors), while Hruby et al. (2006) is an advanced reference for computer scientists. Here is a diagram of an extended REA pattern (from Hruby et al. 2006)

REA-Metamodel.png

REA is a continuing influence on the electronic commerce standard ebXML, with W. McCarthy actively involved in the standards committee. The competing XBRL GL standard however is at odds with the REA concept, as it closely mimics double-entry book-keeping.

REA is now recognised by The Open Group within the TOGAF standard (an industry standard enterprise framework), as one of the modelling tools which is useful for modelling business processes.

Further reading

Related Research Articles

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.

<span class="mw-page-title-main">Data model</span> Model that organizes elements of data and how they relate to one another and to real-world entities.

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure Programing language.

<span class="mw-page-title-main">IDEF</span> Family of modeling languages

IDEF, initially an abbreviation of ICAM Definition and renamed in 1999 as Integration Definition, is a family of modeling languages in the field of systems and software engineering. They cover a wide range of uses from functional modeling to data, simulation, object-oriented analysis and design, and knowledge acquisition. These definition languages were developed under funding from U.S. Air Force and, although still most commonly used by them and other military and United States Department of Defense (DoD) agencies, are in the public domain.

<span class="mw-page-title-main">Data modeling</span> Creating a model of the data in a system

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. It may be applied as part of broader Model-driven engineering (MDD) concept.

<span class="mw-page-title-main">Zachman Framework</span> Structure for enterprise architecture

The Zachman Framework is an enterprise ontology and is a fundamental structure for enterprise architecture which provides a formal and structured way of viewing and defining an enterprise. The ontology is a two dimensional classification schema that reflects the intersection between two historical classifications. The first are primitive interrogatives: What, How, When, Who, Where, and Why. The second is derived from the philosophical concept of reification, the transformation of an abstract idea into an instantiation. The Zachman Framework reification transformations are: identification, definition, representation, specification, configuration and instantiation.

In computing and systems design, a loosely coupled system is one

  1. in which components are weakly associated with each other, and thus changes in one component least affect existence or performance of another component.
  2. in which each of its components has, or makes use of, little or no knowledge of the definitions of other separate components. Subareas include the coupling of classes, interfaces, data, and services. Loose coupling is the opposite of tight coupling.
<span class="mw-page-title-main">Enterprise modelling</span>

Enterprise modelling is the abstract representation, description and definition of the structure, processes, information and resources of an identifiable business, government body, or other large organization.

<span class="mw-page-title-main">ArchiMate</span> Enterprise architecture modeling language

ArchiMate is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way.

<span class="mw-page-title-main">IDEF5</span>

IDEF5 is a software engineering method to develop and maintain usable, accurate domain ontologies. This standard is part of the IDEF family of modeling languages in the field of software engineering.

BORO is an approach to developing ontological or semantic models for large complex operational applications that consists of a top ontology as well as a process for constructing the ontology. It was originally developed as a method for mining ontologies from multiple legacy systems – as the first stage in an architectural transformation or software modernization. It has also been used to enable semantic interoperability between legacy systems. It is described in detail in. It is the analysis method used in the development and maintenance of the U.S. Department of Defense Architecture Framework (DoDAF) Meta Model (DM2), where a data modeling working group of over 350 members was able to systematically resolve a broad spectrum of knowledge representation issues.

Enterprise engineering is the body of knowledge, principles, and practices used to design all or part of an enterprise. An enterprise is a complex socio-technical system that comprises people, information, and technology that interact with each other and their environment in support of a common mission. One definition is: "an enterprise life-cycle oriented discipline for the identification, design, and implementation of enterprises and their continuous evolution", supported by enterprise modelling. The discipline examines each aspect of the enterprise, including business processes, information flows, material flows, and organizational structure. Enterprise engineering may focus on the design of the enterprise as a whole, or on the design and integration of certain business components.

Business semantics management (BSM) encompasses the technology, methodology, organization, and culture that brings business stakeholders together to collaboratively realize the reconciliation of their heterogeneous metadata; and consequently the application of the derived business semantics patterns to establish semantic alignment between the underlying data structures.

<span class="mw-page-title-main">Ontology engineering</span> Field that studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

Design & Engineering Methodology for Organizations (DEMO) is an enterprise modelling methodology for transaction modelling, and analysing and representing business processes. It is developed since the 1980s by Jan Dietz and others, and is inspired by the language/action perspective

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies have become more important in recent times, because the progress in the discovery of the foundations of physics has spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

David S. Frankel is an American Information Technology expert and consultant, known for his work on model-driven engineering and semantic information modeling.

Hans-Erik Eriksson is a Swedish computer scientist, organizational theorist, co-founder of Open Training AB, and author of "Business modeling with UML."

Value Delivery Modeling Language (VDML) is a standard modeling language for analysis and design of the operation of an enterprise with particular focus on the creation and exchange of value.

In natural language processing, linguistics, and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing, and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data Cloud was conceived and is being maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects, and infrastructure efforts since then.