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Active in-database processing to support ambient assisted living systems.

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This study introduces a novel database-centric architecture for smart home and ambient assisted living (AAL) systems. It enhances security and privacy by processing sensitive data within the database, improving system performance and maintainability.

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Area of Science:

  • Computer Science
  • Health Informatics
  • Artificial Intelligence

Background:

  • Existing software architectures for smart homes and ambient assisted living (AAL) systems primarily use database management systems (DBMSs) for data storage only.
  • This approach presents limitations in terms of data processing efficiency, security, and privacy for sensitive user data.

Purpose of the Study:

  • To propose and validate a database-centric architecture for AAL systems that leverages active databases and in-database processing.
  • To enhance the performance, security, and privacy of AAL systems by centralizing data processing within the DBMS.

Main Methods:

  • Development of a database-centric architecture utilizing active databases with triggers for event detection and in-database processing via stored procedures and functions.
  • Implementation and testing of three distinct AAL services to demonstrate the feasibility and flexibility of the proposed architecture.
  • Application of in-database machine learning methods for modeling user behaviors, such as bed-exits and room transitions.

Main Results:

  • The active in-database processing approach successfully detected events like bed-exits and room transitions, and modeled early night behaviors.
  • Centralizing computation within the DBMS eliminated the need to transfer sensitive data externally, significantly improving performance, security, and privacy.
  • The architecture demonstrated improved code reuse, adaptation, and maintenance, crucial for evolving smart home environments.

Conclusions:

  • Database-centric architectures with active and in-database processing offer a scalable, secure, and privacy-preserving solution for AAL systems.
  • DBMSs can effectively address the complex requirements of smart environments in healthcare, including dependability and personalization.
  • This approach is well-suited to the heterogeneous nature of users, needs, and devices characteristic of smart homes and AAL systems.