After applying the computations, the estimated data access time works out to be ~ 47 ms.
在进行计算之后，估计的数据访问时间算出来约为47ms。
With this model, you can simulate the effects a higher near-cache hit will have on the average data access time.
使用这个模型，您可以对较高近距离缓存命中率对平均数据访问时间造成的影响进行模拟。
To compute the data access time, you need to know the probabilities for the data existing in the near-cache, in the server cache, and in the database.
要计算数据访问时间，您需要了解数据存在于近缓存中、服务器缓存中、以及数据库中的概率。
Figure 15. Increasing the probability of a near-cache hit, either through larger near caches or affinity routing, reduces the average data access time.
图15.通过较大近缓存或者关联路由，逐渐增加近缓存命中的概率，可以减少平均数据访问时间。
In the initial example above, the reduction of key data access time from 60 ms to 6 ms was a direct result of this caching benefit and the load reduction on the back-end database.
在上例中，关键数据访问时间从60ms减少到6 ms是该缓存带来的直接好处，同时还减少了后端数据库的负载。
The simulation results show that this replication strategy can reduce the average data access time efficiently.
模拟实验的结果显示此副本创建策略可以有效降低数据平均访问时间。
In a mobile computing environment, improving the data access efficiency not only means reducing the data access time, but also needs to decrease the power consumption while accessing data.
在移动计算环境中，提高移动客户机的数据访问效率，不仅意味着减少移动客户机访问数据的响应时间，同时还需要尽量减少移动客户机访问数据过程中的能量消耗。
To further excavate the nesting loop parallelism and the data access-time locality, a linearity cyclic transformation functional module is used to improve the algorithm.
为了进一步地发掘嵌套循环的并行性和数据访问的时间局部性，对算法增加了一个线性循环变换的功能模块来进行改进。
To further excavate the nesting loop parallelism and the data access-time locality, a linearity cyclic transformation functional module is used to improve the algorithm.
为了进一步地发掘嵌套循环的并行性和数据访问的时间局部性，对算法增加了一个线性循环变换的功能模块来进行改进。
应用推荐