Dimensions to categorize architectural models for integrating data sources

• Autonomy
  Autonomy refers to the degree which individual data sources can operate independently.
  • Design autonomy
    The source is independent in data models, naming of the data elements, semantic interpretation of the data, constraints etc.
  • Communication autonomy
    The source is independent in deciding what information it provides to the other components that are part of the integrated system and to which requests it responds.
  • Execution autonomy
    The source is independent in execution and scheduling of incoming requests.
• Heterogeneity
  Heterogeneity refers to the degree of dissimilarity between the component data sources that make up the data integration system. It occurs at different levels. On a technical level, heterogeneity comes from different hardware platforms, operating systems, networking protocols or similar lower-level concepts. On a conceptual level, heterogeneity comes from different programming and data models as well as different understanding and modeling of the same real-world concepts (ex: naming).
• Distribution
  Distribution refers to the physical distribution of data over multiple sites.
  • Client/Server
    Server does data management, client provides user interface.
  • Peer-to-Peer (fully distributed)
    Each machine has full functionality of data management.
• Transparency
  Transparency refers to the separation of higher-level semantics of a system from lower-level implementation issues. A transparent system hides the implementation details from users.