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Hence, the performance of the selected cache strategies is investigated with varying scope distribution in the context requests.
The discretized Pareto distribution has been selected because it allows us to model scope distribution in context queries with tuneable parameters.
Twelve different scopes are used in this experiment and the scope distribution in context queries is controlled by changing the Pareto shape parameter α while the scale parameter ξ is kept constant at 1.
However, it is evident by considering the results in Figure 4 that OF delivers a better query satisfaction time when the scope distribution in the contextual queries is biased towards SV scopes.
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Since caches are practically limited in size, cache replacement policies have to be employed during the context query-response and the variances in scope distributions in the queries, rate of the queries and validity periods of context scopes greatly influence the effectiveness of the cache replacement policies.
SE replacement policy and cache-hit rate of SV vs. LV in different scope distribution scenarios.
OF replacement policy and cache-hit rate of SV vs. LV in different scope distribution scenarios.
In response, the NYC Department of Health and Mental Hygiene (DOHMH) implemented a multifaceted surveillance approach to understand the severity of illness, and the scope, distribution, and impact of nH1N1 in NYC [3], [5].
This use of two different cache replacement policies suited to the scope validity durations of the data items results in an improved performance and provides a fairly constant mean query satisfaction time across all scope distribution patterns.
The query satisfaction times in case of the bipartite cache with fixed partition sizes only vary by a 1.45% (262ms to 265ms) as the scope distribution varies between SV and LV scoped queries i.e. it provides a fairly consistent performance.
The chart in Figure 9 shows that OF cache replacement policy provides a better query satisfaction time when the context queries are focussed towards SV scopes while SE policy provides a better query satisfaction time when the query scope distribution is biased towards LV scopes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com