1,720,965 research outputs found
Coherence in Data Schema Transformation
No full textThis chapter focuses on change in the information system’s Conceptual Schema in its operational life cycle phase, introducing Semantic Change Patterns as a novel notion in Conceptual Schema evolution. Each pattern outlines a coherent way to accommodate new information needs, both on the level of the existing data structure, and on the level of the data instances (data coercion). An initial catalogue of Semantic Change Patterns is proposed, based on actual schema changes observed in business cases. The catalogue exposes some of the schema evolution expertise that can be found in maintenance practice.</jats:p
Long-Term Evolution of a Conceptual Schema at a Life Insurance Company
No full textEnterprises need data resources that are stable and at the same time flexible to support current and new ways of doing business. However, there is a lack of understanding how flexibility of a Conceptual Schema design is demonstrated in its evolution over time. This case study outlines the evolution of a highly integrated Conceptual Schema in its business environment. A gradual decline in schema quality is observed: size and complexity of the schema increase, understandability and consistency decrease. Contrary to popular belief, it is found that changes aren’t driven only by “accepted” causes like new legislation or product innovation. Other change drivers are identified like error correction, changing perceptions of what the information need of the business is and elimination of derived data. The case shows that a real Conceptual Schema is the result of “objective” design practices as well as the product of negotiation and compromise with the user community.</jats:p
A Method of Ease Schema Evolution
No full textMaintenance on the Conceptual Schema of a database is necessary when the current data structure can’t meet changed functional requirements. An additional requirement, not expressed in the demand for change, is to minimize the impact of change. The problem of minimizing impact of change on the data is often postponed to the implementation phase when data have to be migrated into the new structure. We propose a method to address the problem of Conceptual Schema evolution in an earlier phase, and introduce the notion of Majorant Schema to support the application of the method. The advantage of the approach is that a more graceful schema evolution is ensured because a broader range of design alternatives is investigated. Other benefits are the early attention for the impact of change, and a better preparation of the data migration effort. Experiences show that the method is primarily suited to minor changes.</jats:p
Long-Term Evolution of a Conceptual Schema
No full textEnterprises need data resources that are stable and at the same time flexible to support current and new ways of doing business. However, there is a lack of understanding how flexibility of a Conceptual Schema design is demonstrated in its evolution over time. This case study outlines the evolution of a highly integrated Conceptual Schema in its business environment. A gradual decline in schema quality is observed: size and complexity of the schema increase, understandability and consistency decrease. Contrary to popular belief, it is found that changes arent driven only by accepted causes like new legislation or product innovation. Other change drivers are identified like error correction, changing perceptions of what the information need of the business is and elimination of derived data. The case shows that a real Conceptual Schema is the result of objective design practices as well as the product of negotiation and compromise with the user community.</jats:p
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