Domain-knowledge-guided schema evolution for accounting database systems

The static meta-data view of accounting database management is that the schema of a database is designed before the database is populated and remains relatively fixed over the life cycle of the system. However, the need to support accounting database evolution is clear: a static meta-data view of an accounting database cannot support next generation dynamic environment where system migration, organization reengineering, and heterogeneous system interoperation are essential. This paper presents a knowledge-based approach and mechanism to support dynamic accounting database schema evolution in an object-based data modeling context. When an accounting database schema does not meet the requirements of a firm, the schema must be changed. Such schema evolution can be realized via a sequence of evolution operators. As a result, this paper considers the question: what heuristics and knowledge are necessary to guide a system to choose a sequence of operators to complete a given evolution task for an accounting database? In particular, we first define a set of basic evolution schema operators, employing heuristics to guide the evolution process. Second, we explore how domain-specific knowledge can be used to guide the use of the operators to complete the evolution task. A well-known accounting data model, REA model, is used here to guide the schema evolution process. Third, we discuss a prototype system, REAtool, to demonstrate and test our approach.