An REA Ontology Based Model for Representing Big Data, Analytics, and Information Integrity Issues

• In Event: Design Science and Field Studies

Abstract

This paper uses McCarthy’s (1982) Resource, Events, Agents (REA) ontology to model the associations between the transaction processing components of an accounting information system and Big Data elements. The direct and indirect associations between various Big Data elements and accounting information artifacts are specified using REA primitives. These specifications, in the form of MapReduce-related patterns, connect Big Data elements to traditional accounting information systems. The model also informs Big Data related analytics for accounting decision making by specifying associations between Big Data elements and enterprise system components using the four REA relationship primitives of stock-flow, duality, control, and participation. From an information integrity perspective, the proposed model based on the REA ontology informs the degree of information integrity required of Big Data elements: those that are more (less) proximal to accounting transactions have higher (lower) information integrity requirements. The REA ontology based model presented in this paper can be used by organizations to formalize the associations between Big Data elements in their environment and their accounting information artifacts, by accountants to apply analytics to detect meaningful patterns in accounting data for prescriptive decision making, and by auditors to determine which Big Data elements need scrutiny from an assurance perspective.

Author

• Uday S Murthy, University of South Florida