In real-time systems that have to adapt themselves in bound periods of time, any optimization algorithm must meet correctness, completeness, and efficiency requirements, preferably with a high degree of optimality.
Therefore, in designing a semantic relation system for CPCs, we encounter the following problems: How many relations are needed to meet the completeness criterion, that is, are all semantic relations between CPCs covered by the proposed classification system?
The main difference between these works and our proposal is that they consider non-functional properties on feature models for design decisions, while we address runtime adaptation where configuration selection algorithms must meet correctness, completeness, and efficiency requirements.
To meet the completeness criterion, we have tried to identify as many semantic relations as possible that CPCs may have and we define the relation of elaboration and addition rather flexible and inclusive, which makes the completeness criterion easier to be achieved.
For women residing within five miles of two or three stations, data from one station only were used to generate exposure averages if the other stations did not meet the completeness criteria.
If none of the stations met the completeness criteria, the value for the exposure period was set to missing.
If no stations met the completeness criteria, the value for the exposure period was set to missing.
Five PM2.5 monitors at four different locations (sites) and two NO2 monitors at two different locations met our completeness criteria standards.
Data points not meeting this completeness criterion are classified as the soft data points p s, with an uncertainty attributed to the incompleteness of intended sampling.
Seventy-two percent (586 of 809) of the returned questionnaires met our completeness criterion of 80% for inclusion in the IAQ Practice Index calculation.
