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Applying object orientation to the task of modeling the value-added processes of

The Resource-Event-Agent (REA) enterprise model is a widely accepted framework for the design of the accountability infrastructure of enterprise information systems. Policy-level specifications define constraints and guidelines under which an enterprise operates, and they are an extension to the REA enterprise model, adding the “what should, could, or must be” to the “what is.” This paper aims both at comprehensive understanding of policy-level definitions as part of REA enterprise systems and at understanding of the semantic constructs that enable such definitions. We first explore two distinctive semantic abstractions essential to policy-level specifications: typification and grouping. The typification abstraction links instances of an object class to concepts for which they are concrete realizations, while the grouping abstraction aggregates objects into collections. We next present a number of patterns for the semantic modeling of policies. Following, we look at policy-level applications for REA enterprise information systems. We explore type and grouping definitions for the REA primitives (resource, event, agent) and discuss enterprise applications for three different kinds of policy definitions: knowledge-intensive descriptions, validation rules, and target descriptions. Our discussion of specific enterprise applications includes internal control applications (e.g., limit checks), variance analysis based on standard specifi-cations (e.g., bills of materials), and budgeting applications.

A limitation of existing accounting systems is their lack of knowledge sharing and knowledge reuse, which makes the design and implementation of new accounting systems time-consuming and expensive. An important requirement for knowledge sharing and reuse is the existence of a common semantic infrastructure. In this article we use McCarthy's (1982) Resource-Event-Agent (REA) model as a common semantic infrastructure in an accounting context. The objective is to make knowledge-intensive use of REA to share accounting concepts across functional boundaries and to reuse these concepts in different applications and different systems, an approach we call augmented intensional reasoning. Intensional reasoning is the active use of conceptual structures in information systems operations such as design and information retrieval. For augmented intensional reasoning, the conceptual structures are extended with domain-specific REA knowledge. Sections II and III describe different dimensions of augmented intensional reasoning: the REA primitives, the technological features needed to support augmented intensional reasoning, the need for epistemologically adequate representations to make augmented intensional reasoning feasible, and the practical necessity of implementation compromises. Sections IV and V explore two uses of augmented intensional reasoning: design and operation of knowledge-based accounting systems. The example in Section V explains how augmented intensional reasoning works: (1) define the conceptual schema, (2) structure the conceptual schema in terms of REA (knowledge augmentation), (3) define a shareable and reusable accounting concept (claim), and (4) use the concept (claim) to derive information in different accounting cycles (revenue and acquisition).