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then we would see that we are integrating an expression of the form (x) (x) (which we in fact encountered among our differentiation rules in Section 2.5). As we know, expressions like this arise from differentiating log (x). Returning to the original problem, we pose our initial guess as log[x 2 + 3]. Differentiation of this expression gives the answer 2x/[x 2 + 3]. This is close to what we want, but we must adjust by a factor of 1/2. We write our nal answer as x 1 dx = log[x 2 + 3] + C. 2 x2 + 3 You Try It: Calculate the inde nite integral xe3x EXAMPLE 4.3
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If these two impedances are in parallel, then the total impedance is 1 1 1 1 1 j C(R + j L) + = = + j C = + Z Z1 Z2 R + j L R + j L R + j L = 1 2 LC + j RC R + j L
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a sphere of radius r is 4 r 3 /3. This formula is consistent with the answer we obtained in the last example for r = 1. Use the method of this section to derive this more general formula for arbitrary r .
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Export box make sure Selected Only is checked and that you ve chosen the same font as the one you exported the first part of the logo to.
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At the time of this writing, many network equipment vendors are offering EoF and EoWDM solutions. These vendors range from optical vendors providing related transport, switching, and transponder solutions to Layer 2/3 switch vendors directly integrating optics onto their switching systems. Some of the key ONE vendor offerings are summarized in Table 8.10.
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Dockers can be opened using shortcut keys, menu commands, or through toolbars. Most dockers are found on the Window | Dockers menu, but some such as dockers dealing with text formatting are found on the Text menu and the text-related Property Bar. For example, to open the Contour Docker, choose Window | Dockers | Contour, or press CTRL +F9. To open the Character Formatting docker you can choose Text | Character Formatting from the menu, or press CTRL+T; additionally you can click on the Character Formatting button on the Property Bar after you ve chosen the Text Tool on the Toolbox. Again, the Property Bar is contextual. Dockers open to their last-used screen position and state, either docked or undocked, open or rolled up. While docked, they are by default attached to the right side of your application window. Alternatively, dockers can be positioned on the left side of the screen or anchored on both sides of the screen with your document window in the middle, if that suits you best. While undocked, dockers float above the document window and can be positioned anywhere on your monitor screen(s). Docked or floating is not an all or nothing choice; you can have some dockers docked and some floating at the same time. The only situation you can t have is more than one copy of a specific docker open at one time. Figure 2-3 shows examples of docked and floating dockers. Floating or not, dockers feature a common look. Each has a title bar, a Close Docker group button which closes only that specific docker, and a Roll-up/Roll-down Docker button, which is used to toggle the display between the title bar-only state and a fully
This program uses fgets( ) to display the contents of the text file specified in the first command line argument:
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Part I:
usiness intelligence covers a range of technologies, and in this book business intelligence is a generic term covering the data warehouse as well as the tools and applications used to view the data from the warehouse. While the major focus of this book is on PerformancePoint Server and ProClarity, which allows users to consume data, there would be nothing to view without first building a data warehouse or data mart. Before diving into the process of building the data warehouse, it s important to define the difference between a data warehouse and a data mart. The difference between the two is one of scope: marts cover individual business areas, while warehouses cover the entire business. Data marts focus on a particular business function, such as finance, manufacturing, sales and marketing, and so forth. There is a raging, often religious debate about the proper method of building a warehouse: whether to build a number of marts and then roll them up into a warehouse, or build a warehouse and then split out data marts as necessary. This debate is beyond the scope of this book, and the good news is that the decision is unimportant for the purposes of this book. The process of building a data mart is identical to the process of building a data warehouse; only the scope is different. Therefore, while most companies have one or more marts and not a true warehouse, this book will use the term data warehouse as a generic term to cover both warehouses and marts. Because the process is the same for building both marts and warehouses, this chapter will describe that process and cover the aspects of the business intelligence process that build the foundation for delivering value to the organization. The process of building a warehouse involves a number of steps, each of which contains an abundance of details. While entire books are written on the various aspects, this chapter will seek to distill the most important parts of the warehouse building process into an overview that will explain the importance of the decisions made here and how they can affect the delivery of business intelligence information through PerformancePoint Server and ProClarity. It is important to realize that building a data warehouse is not an easy task and many decisions must be made, some of which involve tradeoffs. The overall flow of building a warehouse is generally described as shown in Figure 3-1. You can see that the data is located in its source systems, migrated into a relational data warehouse, and then turned into cubes for consumption by end user tools. Each part of this process contains complexities and subtleties that can greatly affect the usability and ultimate acceptance of the warehouse by business users. It is therefore critical that these pieces be taken seriously and that proper planning be done in each phase. The first phase will encompass several aspects of the system design and will lay the foundation for all the analysis that will be performed.
places the value of num into q. Following through with this example, q has the value 100 because 100 is stored at location 2000, which is the memory address that was stored in p. The operation of the * can be remembered as at address. In this case the statement could be read as q receives the value at address p.
The next stage after (or towards the end) of the design cycle is QA and verification testing. This involves comprehensive testing to ensure correct function of an NE to
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