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The Aggregate category, unlike the others, has multiple options that you can choose from (a total of nine options): Sum, Avg, Min, Max, Count, StDev, Var, First, and Last. These options appear as the second through tenth options in the dropdown list. Each option performs an operation on your data (check out Table 23-2 for how you can use each option) and supplies the new data to a cell in the resulting dynaset. Aggregate options are what database designers think of when they hear the words total query. Each of the options performs a calculation against a field in the QBE pane of the query and returns a single answer in the dynaset. For example, you may want to determine the maximum (Max) and minimum (Min) weight of each animal in the Type of Animal field in the Pets table. There can only be one maximum weight for all the animals. Several animals may have the same maximum weight, but only one weight is the heaviest. Another example of a total query would be if you wanted the total number (Count) of animals in the Pets table (again, the query returns a single answer). You can use these aggregate options to solve these types of queries. barcode reader sdk
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Manual Manual Automatic Automatic Automatic Automatic Automatic Automatic Automatic Automatic Automatic Automatic Automatic Automatic Manual Automatic Manual Automatic Automatic Manual Automatic Automatic
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The Javadoc view, which is unique to the Eclipse Java development plug-in, enables you to view the output of Javadoc, a tool that creates documentation from specially formatted Java source code comments. The Declaration view works in combination with the class browser to show you the complete declaration of methods and data types. The Console view shows the actual output of the Java program. While much of Eclipse was designed to support developing applications in the Java language, plugins provide support for C, C++, Perl, Python, and other languages.
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Figure 4.7 - A repeater
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You will see two changes in this file. The first change is just a couple of import statements that include the ActionError and ActionErrors classes. The second change is the actual error reporting section. In this section, we test the value of the price variable. If it is null which it will be if any value other than SUNW was entered then we need to report an error. This is accomplished by first setting the target to failure; then by creating an ActionErrors collection and adding an ActionError object to the collection. The last thing to note about these changes is how the errors are reported back to the client. This is done using the Action.saveErrors() method, as shown here:
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8.3.1 Data Representation, Data Structures, and Transmission Modes
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Part I
proposed, the designers turned to the girls in a spirit of participatory design in order to elicit their responses.
Select Class Model from the Other category of file template types, as shown in Figure 3.30. Figure 3.30 Selecting class model
the convolution of h(t) and the signal s(t) as described in Eq. (5-4) for the discrete sequences x (m) and h(m). The same fold and slide procedure is used in Eq. (8-3), where the symbol H means Hilbert and (not the same as asterisk *) is the convolution operator: H [s(t)] = s (t) = h(t) s(t) = 1
Choosing and Installing a Linux Distribution Metrics Reliability Metrics on the reliability and accuracy of the metrics themselves will be needed in order to make appropriate decisions. If the metrics of a critical function are only 90% reliable and 60% accurate, additional actions will be required to achieve high assurance levels since the information can only be relied on 45% of the time. Another consideration for the decision maker will be whether the metrics will indicate false positives or false negatives, or whether in some cases they will fail to show anything. Metrics need to be tested periodically to provide assurance of functionality, reliability, and accuracy. Depending on the metric, reliability can be measured in a number of ways. Repeatability and consistency are useful for measuring both reliability and accuracy. For technical metrics, testing can usually be automated to provide assurance of consistency. Other gauges of reliability can range from statistical sampling and analysis over a large number of measurements to comparison with some sort of standard. Measures may be evaluated against outcomes to determine reliability and consistency. That is to say, if a particular metric value consistently results in the same outcome, it is reliable. If every time the fuel gauge shows empty, the vehicle ceases operating, there is a level of assurance of the reliability of the gauge. Conversely, if periodically it shows an amount of fuel when there is none, prudence dictates that some other metric should be used such as a dipstick in the gas tank. Procedure Functionality, Efficiency, and Appropriateness Finally, it is necessary to consider the possibility that if there are problems with compliance then a particular procedural control is poorly designed, difficult to perform, or inefficient. An analysis of the control objectives may be needed to ensure that they are properly aligned with business and security requirements. Research indicates that, more often than not, poor performance by employees is the result of bad process design. As a result, the procedure may need to be tested and evaluated from a process standpoint and, perhaps, be redesigned or possibly automated. In most organizations, procedures that more or less work are typically not subject to review. If compliance is found to be a problem, a good candidate for the root cause will be poor procedures. If control objectives have not been defined and the assets not classified, it will be difficult to determine appropriate procedural controls, however. With the assumption that procedural controls are documented, one measure is the completeness, accuracy, consistency, and conformance to standards (and, therefore, policy) of the procedure itself. Another measure is the ability of someone unfamiliar with the procedure to accurately accomplish the task using the written procedure. Tactical Performance Measures Historical data, to the extent it is available, can be useful in determining how effective existing performance measures are. Analysis of prior events and postmortems can yield information on issues such as:
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