5.2.1.

text/evaluation/thething.tex

@@ -17,18 +17,24 @@ With the experiments on the synthetic data sets (Section~\ref{subsec:lmdk-expt-c
 \subsection{Budget allocation schemes}
 \label{subsec:lmdk-expt-bgt}
 
-Figure~\ref{fig:real} exhibits the performance of the three mechanisms: Skip, Uniform, and Adaptive, for the three data sets that we study.
+Figure~\ref{fig:real} exhibits the performance of the three mechanisms, Skip, Uniform, and Adaptive applied on the three data sets that we study.
 % For the Geolife data set (Figure~\ref{fig:geolife}), Skip has the best performance (measured in Mean Absolute Error, in meters) because it invests the most budget overall at every regular event, by approximating the {\thething} data based on previous releases.
 % Due to the data set's high density (every $1$--$5$ seconds or every $5$--$10$ meters per point) approximating constantly has a low impact on the data utility.
 % On the contrary, the lower density of the T-drive data set (Figure~\ref{fig:t-drive}) has a negative impact on the performance of Skip.
-For the Copenhagen data set (Figure~\ref{fig:copenhagen}), Adaptive has a constant overall performance and performs best for $0$\%, $60$\%, and $80$\% {\thethings}.
-We notice that for $0$\% {\thethings}, it achieves better utility than the event-level protection.
-The Skip model excels, compared to the others, at cases where it needs to approximate $20$\%--$40$\% or $100$\% of the times.
-The combination of the low range in HUE ($[0.28$, $4.45]$ with an average of $0.88$kWh) and the large scale in the Laplace mechanism, results in a low mean absolute error for Skip (Figure~\ref{fig:hue}).
+
+For the Copenhagen data set (Figure~\ref{fig:copenhagen}), Adaptive has a constant\kat{it is not constant, for 0 it is much lower} overall performance and performs best for $0$\%, $60$\%, and $80$\% {\thethings} \kat{this is contradictory: you say that it is constant overall, and then that it is better for certain percentages. }.
+We notice that for $0$\% {\thethings}, it achieves better utility than the event-level protection.\kat{what does this mean? how is it possible?}
+The Skip model excels, compared to the others, at cases where it needs to approximate $20$\%--$40$\% or $100$\% of the times.\kat{it seems a little random.. do you have an explanation? (rather few times or all?)}
+
+The combination of the small range of measurements in HUE ($[0.28$, $4.45]$ with an average of $0.88$kWh) and the large scale in the Laplace mechanism, results in a low mean absolute error for Skip (Figure~\ref{fig:hue}).
 In general, a scheme that favors approximation over noise injection would achieve a better performance in this case.
-However, the Adaptive model performs by far better than Uniform and strikes a nice balance between event- and user-level protection for all {\thething} percentages.
+\kat{why?explain}
+However, the Adaptive model performs by far better than Uniform and strikes a nice balance\kat{???} between event- and user-level protection for all {\thething} percentages.
+
 In the T-drive data set (Figure~\ref{fig:t-drive}), the Adaptive mechanism outperforms Uniform by $10$\%--$20$\% for all {\thething} percentages greater than $40$\% and Skip by more than $20$\%.
-The lower density (average distance of $623$m) of the T-drive data set has a negative impact on the performance of Skip.
+The lower density (average distance of $623$m) of the T-drive data set has a negative impact on the performance of Skip; republishing a previous perturbed value is now less accurate than perturbing the new location.
+
+
 \begin{figure}[htp]
 	\centering
 	\subcaptionbox{Copenhagen\label{fig:copenhagen}}{%
@@ -45,8 +51,8 @@ The lower density (average distance of $623$m) of the T-drive data set has a neg
 	\label{fig:real}
 \end{figure}
 
-In general, we can claim that the Adaptive is the most reliable and best performing mechanism with minimal tuning, if we take into consideration the drawbacks of the Skip mechanism mentioned in Section~\ref{subsec:lmdk-mechs}.
-Moreover, designing a data-dependent sampling scheme would possibly result in better results for Adaptive.
+In general, we can claim that the Adaptive is the most reliable and best performing mechanism with minimal tuning\kat{what does minimal tuning mean?}, if we take into consideration the drawbacks of the Skip mechanism mentioned in Section~\ref{subsec:lmdk-mechs}. \kat{you can mention them also here briefly, and give the pointer for the section}
+Moreover, designing a data-dependent sampling scheme \kat{what would be the main characteristic of the scheme? that it picks landmarks how?} would possibly\kat{possibly is not good enough, if you are sure remove it. Otherwise mention that more experiments need to be done?} result in better results for Adaptive.
 
 
 \subsection{Temporal distance and correlation}