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Summary
This summary is machine-generated.

Evolving database-dependent applications is challenging. This paper introduces a data-centric architecture enabling seamless co-evolution of database components and applications through centralized models and adaptive clients.

Keywords:
application-database co-evolutionmodel managementschema evolutionsoftware ecosystems

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Area of Science:

  • Computer Science
  • Data Management
  • Software Engineering

Background:

  • Database evolution is complex, especially when applications depend on them.
  • Scientific research increasingly relies on evolving database systems for data management.

Purpose of the Study:

  • To present a novel architecture for data-centric ecosystems that facilitates seamless co-evolution.
  • To address the challenges of evolving database-dependent applications in scientific contexts.

Main Methods:

  • Centralizing models and mappings at the data service.
  • Pushing model-adaptive interactions to database clients.
  • Utilizing boundary objects for stable interfaces where applications cannot adapt.

Main Results:

  • Demonstrated seamless co-evolution of ecosystem components.
  • Enabled stable application interaction through boundary objects.
  • Integrated schema modification and model management for ecosystem evolution.

Conclusions:

  • The proposed data-centric architecture effectively manages the co-evolution of database systems and applications.
  • This approach enhances the adaptability and maintainability of complex, data-driven scientific ecosystems.