<p>An embedded system is an application-specific system that is typically dedicated to performing a particular task. Majority of embedded systems are also real-time, implying that timeliness in the system need to be enforced. An embedded system needs to be enforced efficient management of a large amount of data, including maintenance of data freshness in an environment with limited CPU and memory resources. Uniform and efficient data maintenance can be ensured by integrating database management functionality with the system. Furthermore, the resources can be utilized more efficiently if the redundant calculations can be avoided. On-demand updating and active behavior are two solutions that aim at decreasing the number of calculations on data items in embedded systems.</p><p>COMET is a COMponent-based Embedded real-Time database, developed to meet the increasing requirements for efficient data management in embedded real-time systems. The COMET platform has been developed using a novel software engineering technique, AspeCtual COmponent-based Real-time software Development (ACCORD), which enables creating database configurations, using software components and aspects from the library, based on the requirements of an application. Although COMET provides uniform and efficient data management for real-time and embedded systems, it does not provide support for on-demand and active behavior.</p><p>This thesis is focusing on design, implementation, and evaluation of two new COMET configurations, on-demand updating of data and active behavior. The configurations are created by extending the COMET component and aspect library with a set of aspects that implement on-demand and active behavior. The on-demand updating aspect implements the ODDFT algorithm, which traverses the data dependency graph in the depth-first manner, and triggers and schedules on-demand updates based on data freshness in the value domain. The active behavior aspect enables the database to take actions when an event occurs and a condition coupled with that event and action is fulfilled.</p><p>As we show in the performance evaluation, integrating on-demand and active behavior in COMET improves the performance of the database system, gives a better utilization of the CPU, and makes the management of data more efficient.</p>

An embedded system is an application-specific system that is typically dedicated to performing a particular task. Majority of embedded systems are also real-time, implying that timeliness in the system need to be enforced. An embedded system needs to be enforced efficient management of a large amount of data, including maintenance of data freshness in an environment with limited CPU and memory resources. Uniform and efficient data maintenance can be ensured by integrating database management functionality with the system. Furthermore, the resources can be utilized more efficiently if the redundant calculations can be avoided. On-demand updating and active behavior are two solutions that aim at decreasing the number of calculations on data items in embedded systems. COMET is a COMponent-based Embedded real-Time database, developed to meet the increasing requirements for efficient data management in embedded real-time systems. The COMET platform has been developed using a novel software engineering technique, AspeCtual COmponent-based Real-time software Development (ACCORD), which enables creating database configurations, using software components and aspects from the library, based on the requirements of an application. Although COMET provides uniform and efficient data management for real-time and embedded systems, it does not provide support for on-demand and active behavior. This thesis is focusing on design, implementation, and evaluation of two new COMET configurations, on-demand updating of data and active behavior. The configurations are created by extending the COMET component and aspect library with a set of aspects that implement on-demand and active behavior. The on-demand updating aspect implements the ODDFT algorithm, which traverses the data dependency graph in the depth-first manner, and triggers and schedules on-demand updates based on data freshness in the value domain. The active behavior aspect enables the database to take actions when an event occurs and a condition coupled with that event and action is fulfilled. As we show in the performance evaluation, integrating on-demand and active behavior in COMET improves the performance of the database system, gives a better utilization of the CPU, and makes the management of data more efficient.