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You can choose whether to base the query method you are adding on a new SQL statement, on a new stored procedure, or on an existing stored procedure that is already defined in the database For this example, let's define a new SQL statement 3 After selecting or accepting the Use SQL statements option, click the Next button This displays the Choose a Query Type page shown in Figure 212



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In this definition, B is the base of the logarithm In computer science, when the base is omitted, it defaults to 2, which is natural for several reasons, as we show later in the chapter We prove one mathematical theorem, Theorem 64, to show that, as far as Big-Oh notation is concerned, the base is unimportant and also to show how relations that involve logarithms can be derived

Figure 66: XML file connection settings to opt in or out of including the data as a file in your template

The base does not matter: For any constant B > 1, logBN= O(1og N)

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Let logBN = K Then BK = N Let C = log B Then 2C = B Thus BK = ( 2 C ) K= N Hence, we have 2CK = N, which implies that log N = CK = C log,N Therefore logsN = (logN)/(logB), thus completing the prooj

from the button in Figure 65 as well as the Tools menu in design mode) Since we want our XML data to be live, that is, if the data is updated we want our form to re ect the updates, we instead choose the option Access the data from the speci ed location The checkbox to Store a copy of the data for of ine use should not be checked This is for the cached query feature, which we will discuss in 7 when we cover the topic of of ine query support

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In the rest of the text, we use base 2 logarithms exclusively An important fact about the logarithm is that it grows slowly Because 21 = 1024, log 1024 = 10 Additional calculations show that the logarithm of 1,000,000 is roughly 20 and that the logarithm of 1,000,000,000 is only 30 Consequently, performance of an O(N log N ) algorithm is much closer to a linear O ( N ) algorithm than to a quadratic O(N2)algorithm for even moderately large amounts of input Before we look at a realistic algorithm whose running time includes the logarithm, let us look at a few examples of how the logarithm comes into play

When including XML data as a resource le in the form template, InfoPath will try to keep the original le name For example, if you pointed to \\MyComputer\MyShare\MyDataxml and chose the Include the data as a resource le in the form template option, a le called MyDataxml would be added automatically to the form template resource les If the XML data comes from a Web site with characters such as slashes and question marks that cannot be used in a le name, InfoPath will attempt to use a base le name For example, http:// MyServer/MyDataaspx Type=1 would yield a le with resource name MyDataaspx

A 16-bit s h o r t integer represents the 65,536 integers in the range -32,768 to 32,767 In general, B bits are sufficient to represent 2B different integers Thus the number of bits B required to represent N consecutive integers satisfies the equation 2B 2 N Hence we obtain B 2 log N, so the minimum num ber of bits is [log ~ 1(Here [XI is the ceiling function and represents the smallest integer that is at least as large as X The corresponding floor funcrepresents the largest integer that is at least as small as X) tion

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Suppose that we start with $1 and double it every year How long would it take to save a million dollars In this case, after 1 yr we would have $2; after 2 yr, $4; after 3 yr, $8, and so on In general, after K years we would have 2K dollars, so we want to find the smallest K satisfying 2 2 N This is the same K equation as before, so K = [log ~ 1After 20 yr, we would have more than a million dollars The repeated doubling principle holds that, starting from 1, we can repeatedly double only [log times until we reach N

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