本文提出了一种多输入-多输出系统动态载荷的优化估计方法。
An optimal method is developed to estimate the dynamic loads for systems subjected to multiple inputs.
采用加权的均方误差准则来优化估计模型的参数，实现对测量序列的抗扰最佳估计。
To obtain the best estimation of anti-jamming for measured series, the rule of weighted-square error is introduced to optimize the parameters of estimating model.
利用码间串扰量的度量准则，对不同的成形滤波函数研究了插值滤波器的参数优化估计及其抗时钟抖动性能。
By using the measure criterion of intersymbol interference, the parameter optimization and robustness of timing jitter of interpolation filter for a few kinds of shaping functions has been studied.
这已经是一个较好的值，因为它可以保证优化器在使用分位数统计信息的情况下对确定过滤因子的估计误差最大只有5%。
This is already a good value because it guarantees that the optimizer, by using the quantile statistics, only has an estimated maximum error of 5% for the determined filter factors.
因此，如果优化器的估计是准确的，则此访问路径应该是一个非常有效的访问路径。
Therefore, if the optimizer's estimate is accurate, this access path should be a very efficient access path.
每一个节点上显示的数字都是优化器对基础数据源或操作节点的基数的估计。
The number shown in each node is the optimizer's estimate for the cardinality of the underlying data source or operation node.
查询优化器根据从每个表检索的行数估计查询成本。
The query optimizer bases query-cost estimates on the number of rows to be retrieved from each table.
当查询性能中出现例外情况时，优化指南和统计视图特性对于弥补访问计划估计的不准确性非常有用。
Optimization guidelines and statistical views are useful features to compensate the access plan evaluations when exceptions in query performance occur.
优化器估计的总行数是 2.176。
The total number of rows, estimated by the optimizer, is 2.176.
有了新的优化框架，4.1发行版系列引入了大量的优化，例如改进的概要分析(profiling)支持和更准确的分支可能性估计。
With the new optimization framework in place, the 4.1 release series introduced a larger number of optimizations, such as improved profiling support and more accurate branch probability estimation.
基于优化器的估计，在目标索引键范围内有8个合格的RID。
Based on the optimizer's estimate, there are eight qualifying RIDs within the target index key range.
当我刚开始这个实验，解释应用各种锁优化的Hotspot的有效性的时候，我估计它将花费我几个小时的时间，最终这会丰富我的blog的内容。
When I first started this exercise to examine the effectiveness of Hotspot applied lock optimizations, I figured it would take a couple hours of my time and in the end I've have a useful blog entry.
对于访问计划的每个操作符，优化器将估计该操作符的基数输出。
At each operator in the access plan, the optimizer estimates the cardinality output from the operator.
基数估计是这样一种过程：在应用了谓词或执行了聚集之后，优化器使用统计信息确定部分查询结果的大小。
Cardinality estimation is a process by which the optimizer USES statistics to determine the size of partial query results after predicates are applied or aggregation is performed.
然后，这些信息被包括进来，用于帮助优化器在为那些符合条件的查询(这些查询不需要直接引用视图)的选择性估计和基数估计计算成本时做决定。
This information can then be included as part of the optimizer decision during costing for selectivity and cardinality estimates for qualified queries which do not need to reference the view directly.
优化器凭借精确的基数估计值来准确计算出每一个待定查询访问计划的成本。
The optimizer is dependent on accurate cardinality estimates to properly compute the cost of each query access plan considered.
您可以利用DB2 9.5 中的多列统计信息的扩展用途来为优化器提供更多的信息，从而使优化器更好地估计基数，选择最佳的查询访问计划。
You can leverage the extended use of multi-column statistics in DB2 9.5 to provide the optimizer more information to better estimate the cardinality in order to choose an optimal query access plan.
这个示例解释说明了部分统计信息对于优化器估计基数的能力的影响。
This example illustrates the effect that partial statistics have on the ability of the optimizer to estimate the cardinality.
优化器凭借精确的基数估计值来准确计算出每一个待定查询访问计划的成本。
The optimizer depends on accurate cardinality estimates to properly compute the cost of each query access plan considered.
统计视图和REOPT都使优化器可以计算出更精确的基数估计，而后选择一个最佳查询执行计划。
Both statistical views and REOPT allow the optimizer to compute a more accurate cardinality estimate and consequently choose an optimal query execution plan.
每个断言的筛选因子表示优化器估计符合断言的数据的比例。
The Filter factor for each predicate indicates the proportion of data the optimizer estimates will satisfy the predicate.
然后，优化器可以将输入变量的值与编目统计进行比较，并为谓词计算出一个更好的选择估计。
The optimizer can then compare the input variable values to catalog statistics and compute a better selectivity estimate for predicates.
生成访问计划，查看优化器对该查询返回的总基数(行数)的估计值。
Generate the access plan and see what the optimizer estimates the total cardinality (Number of rows) returned by the query will be.
优化器将选择该查询计划，因为其执行的估算成本是所有被估计划中最低的。
The optimizer chose this query plan because the estimated cost for its execution was the lowest among all the evaluated plans.
优化器使用该数据模型来估计可以用于解决某个特定查询的其它存取路径的成本。
This data model is used by the optimizer to estimate the costs of alternative access paths that can be used to resolve a particular query.
优化器根据可用的统计信息计算基数的估计值，即预计查询返回的行数。
The optimizer calculates its cardinality estimate — that is, the estimated number of rows returned by the query — based on the statistics available.
换句话说，优化器过高估计了本地谓词对表格的选择性。
In other words, the optimizer overestimated the selectivity of the local predicates on the table.
只要数据分布自上一次RUNSTATS以后没有改变，则优化器的估计可能非常接近实际值。
The optimizer's estimate could be very close to the actual value as long as the data distribution has not changed since the last RUNSTATS.
实际上，通过基本统计信息，DB 2优化器只能估计' VALUE_Z '在COLUMN_Y中出现的频率。
Indeed, from the basic statistics, the DB2 optimizer can only estimate the frequency of 'VALUE_Z' in COLUMN_Y.
如果有了准确的统计信息，那么查询优化器一般能够得出正确的选择性估计。
Given accurate statistics, the query optimizer generally comes up with correct selectivity estimates.
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