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All solid waste materials were within the desired 5 percent con dence interval to validate the grouping of SIC codes. The minimum range was 0.5 percent and the maximum range was 4.6 percent. Table 15.1 displays an example of the results for organic wastes in SIC code group 01: agriculture production crops. The 95 percent con dence interval range of 2.4 percent indicates that 95 percent of the mean percentages of organic waste for SIC code group 01 (agriculture production crops) will be between 19.6 percent and 24.4 percent. The 95 percent con dence interval 2.4 percent is well within the desired range of 5 percent to validate grouping material composition percentages based on SIC codes. One-way ANOVA was applied to examine difference between all 65 SIC code groups and materials. Table 15.2 is the one-way ANOVA for organic waste. As shown in the table, the test statistic (F = 1354.734) is significantly higher than

Message format mismatch Related to protocol mismatch is the problem of a mismatch of message formats between the service provider and the service consumer. This problem refers to the situation where a service provider may be set up to receive messages in one format (such as SOAP), while the service consumer is set up to use another message format (such as Java RMI).

Apple has a new feature for iTunes called Genius Playlists. If the Genius feature is activated in iTunes, it will show up here on your iPad with the symbol shown. NOTE: You must enable Genius Playlists using iTunes on your computer. Check out our iTunes Guide chapter to learn how.

TABLE 15.1 NINETY-FIVE PERCENT CONFIDENCE INTERVAL ON MATERIAL COMPOSITION PERCENTAGE EXAMPLE TO VALIDATE SIC CODE GROUPING (ORGANIC WASTE IN THE SIC CODE GROUP 01) MEAN NUMBER OF PERCENTAGE VARIANCE COMPANIES OF ORGANIC OF ORGANIC SURVEYED WASTE WASTE

95% CONFIDENCE INTERVAL (+/ )

These and other problems have been addressed by further development in the technologies. In particular, the evolution of Enterprise Service Bus patterns solves many of the remaining heterogeneity problems as well as other problems. We discuss these problems and the Enterprise Service Bus in the next chapter.

What the Genius feature does is create a playlist by associating similar songs with the one you re listening to. Unline a random shuffle of music, Genius will scour your music library and then create a new playlist of 25, 50, or 100 songs (you set the Genius features in iTunes on your computer).

2.4%

Genius is a great way to mix up your music and keep it fresh playing the type of music you like but also finding some buried songs that may not be part of your established playlists.

7 provided an overview of the standards generally known as Web Services The main objective of these standards is to provide a solution to the various heterogeneity problems found in large enterprises Toward the end of 7, you learned that Web Services can provide only a part of the solution and that some heterogeneity problems are still left unresolved In particular, quite often there is a need to provide a mechanism for a communication protocol switch and to provide a mechanism for data transformation in order to match the requirements of a service provider with that of a service client In this chapter, we tackle the remaining heterogeneity problems by discussing the Enterprise Service Bus (ESB) pattern ESB provides a comprehensive, scalable way to connect a large number of applications without the need for each pair of applications to make a direct connection.

F critical (F crit = 1.344158) at the 95 percent con dence level. This indicates the SIC code groups generate signi cantly different composition percentages for organic waste. All other waste compositions were also signi cantly different at the 95 percent con dence level. The results of the validations tests indicated that the 65 SIC code groups were effective to group businesses based on waste stream composition percentages (means and variances) at the 95 percent con dence level. The companies within each of the 65 SIC code groups generated equal waste stream composition percentages at the 95 percent con dence level. The 65 SIC code groups generated statistically different composition percentages of solid waste as validated by the ANOVA analysis at the 95 percent con dence level. The purpose of the next characterization phase is to cluster the 65 SIC code groups to determine which SIC code groups generate similar solid waste composition pro les as determined by the means and variances for each material.