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Very often effective presentations can be produced directly from the logical data model. However, sometimes the requirements go beyond the capabilities of declarative templates. For example it may be necessary to perform calculations or text processing. For this reason, the framework has a three tier, Model-View-Control architecture. The rst layer is the logical data model. The second layer contains the business logic which performs any necessary processing. The third and nal layer is the presentation layer where the data is transformed into a suitable format for presentation on the target device. This architecture addresses the separation of concerns issue discussed in Section 8.4.1.1. In the current implementation of the DIWAF, device identi cation uses the RDF-based CC/PP (an open standard from W3C), with an open source Java implementation. In this framework, device pro le information is made available to Java servlets as a collection of attributes, such as screen size, browser name etc. These attributes can be used to inform the subsequent selection and adaptation of content, by combining them in Boolean expressions. Figure 8.4 shows exactly the same content (located

We have used the mean plus or minus one standard deviation here If n = 10, for example, this is the interval (2628, 8372) that contains the values 3, 4, 5, 6, 7, 8 or 60% of the distribution Suppose now that we add and subtract k standard deviations from the mean for the general uniform discrete distribution, then the length of this interval is 2k (n2 1)/12 and the percentage of the distribution contained in that interval is 2k n2 1 12 = 2k n n2 1 12n2

Please note Intermediate and advanced courses require previous attendance of OS Essentials. UI system creation course also requires previous attendance of Application UI course.

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The factor (n2 1)/n2 , however, rapidly approaches 1 as n increases So the percent age of the distribution contained in the interval is approximately 2k/ 12 = k/ 3 for reasonably large values of n If k = 1, this about 577% and if k = 15, this is is about 866%; k of course must be less than 3 or else the entire distribution is covered by the interval

at the same URL) rendered via DIWAF on a standard web browser and on a WAP browser emulator. We have used this framework to support delivery of knowledge to users on a variety of devices in the SEKTAgent system, as discussed later in this chapter. Further details of this approach are available in Glover and Davies (2005).

These community websites offer news, reviews, features and forums, and represent a rich alternative source of information that complements the Symbian Development Network and the development tools publishers. They are good places to keep abreast of new software and, of course, to announce the latest releases of your own applications.

5

We have seen in Section 82 how some semantic search tools use an ontological knowledge base to enhance their search capability We discussed in Sections 83 and 84 the use of natural language generation to describe ontological knowledge in a more natural format and the delivery of knowledge to the user in a format appropriate to the terminal device to which they currently have access In this section, we describe a semantic search agent, SEKTAgent, which brings together the exploitation of an ontological knowledge base, natural language generation and device independence to proactively deliver relevant information to its users Search agents can reduce the overhead of completing a manual search for information The best known commercial search agent is perhaps Google Alerts , based on syntactic queries of the Google index Using an API provided by the KIM system (see Section 82.

So the more standard deviations we add to the mean, the more of the distribution we cover, and this is true for probability distributions in general This really seems to be a bit senseless however since we know the value of n and so it is simple to gure out what percentage of the distribution is contained in any particular interval We will return to con dence intervals later For now, what if we do not know the value of n This is an entirely different matter This problem actually arose during World War II The Germans numbered much of the military material it put into the battle eld They numbered parts of motorized vehicles and parts of aircraft and tanks So, when tanks, for example, were captured, the Germans unwittingly gave out information about how many tanks were in the eld! If we assume the tanks are numbered 1, 2, , n (here is our uniform distribution!) and we have captured tanks numbered 7,13, and 42, what is n This is not a probability problem but a statistical one since we want to estimate the value of n from a sample We will have much more to say about statistical problems in later chapters and this one in particular The reader may wish to think about this problem and make his or her own estimate Notice that the estimate must exceed 42, but by how much We will return to this problem later We now turn to another extremely useful situation, that of sums

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