<p>The amount of data handled by real-time and embedded applications is increasing. This calls for data-centric approaches when designing embedded systems, where data and its metainformation (e.g., temporal correctness requirements) are stored centrally. The focus of this thesis is on efficient data management, especially maintaining data freshness and guaranteeing correct age on data.</p><p>The contributions of our research are updating algorithms and concurrency control algorithms using data similarity. The updating algorithms keep data items up-to-date and can adapt the number of updates of data items to state changes in the external environment. Further, the updating algorithms can be extended with a relevance check allowing for skipping of unnecessary calculations. The adaptability and skipping of updates have positive effects on the CPU utilization, and freed CPU resources can be reallocated to, e.g., more extensive diagnosis of the system. The proposed multiversion concurrency control algorithms guarantee calculations reading data that is correlated in time.</p><p>Performance evaluations show that updating algorithms with a relevance check give significantly better performance compared to well-established updating approaches, i.e., the applications use more fresh data and are able to complete more tasks in time. The proposed multiversion concurrency control algorithms perform better than HP2PL and OCC and can at the same time guarantee correct age on data items, which HP2PL and OCC cannot guarantee. Thus, from the perspective of the application, more precise data is used to achieve a higher data quality overall, while the number of updates is reduced.</p> / Report code: LiU-Tek-Lic-2004:67.

The amount of data handled by real-time and embedded applications is increasing. This calls for data-centric approaches when designing embedded systems, where data and its metainformation (e.g., temporal correctness requirements) are stored centrally. The focus of this thesis is on efficient data management, especially maintaining data freshness and guaranteeing correct age on data. The contributions of our research are updating algorithms and concurrency control algorithms using data similarity. The updating algorithms keep data items up-to-date and can adapt the number of updates of data items to state changes in the external environment. Further, the updating algorithms can be extended with a relevance check allowing for skipping of unnecessary calculations. The adaptability and skipping of updates have positive effects on the CPU utilization, and freed CPU resources can be reallocated to, e.g., more extensive diagnosis of the system. The proposed multiversion concurrency control algorithms guarantee calculations reading data that is correlated in time. Performance evaluations show that updating algorithms with a relevance check give significantly better performance compared to well-established updating approaches, i.e., the applications use more fresh data and are able to complete more tasks in time. The proposed multiversion concurrency control algorithms perform better than HP2PL and OCC and can at the same time guarantee correct age on data items, which HP2PL and OCC cannot guarantee. Thus, from the perspective of the application, more precise data is used to achieve a higher data quality overall, while the number of updates is reduced. / <p>Report code: LiU-Tek-Lic-2004:67.</p>