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This Symbian OS smartphone ships with a PC suite based on SCOM and is fully compatible with the methods described in this book.

So far we have considered discrete random variables, that is, random variables de ned on a discrete or countably in nite set We now turn our attention to continuous random variables, that is, random variables de ned on an in nite, uncountable, set such as an interval or intervals The simplest of these random variables is the uniform random variable

rich behavioral process model based on Process Speci cation Language (PSL) (Gruninger, 2003) FLOWS can be seen as an extension/re nement of OWL-S ontology with a special focus on providing interoperability or semantics to existing standards in Web services area (eg, BPEL, WSDL, etc) Although there are many similarities between FLOWS and OWL-S ontologies, one important difference is the expressiveness of the underling language FLOWS is based on First-Order logic, which means it has a richer, more expressive, support than OWL-S which is based on OWLDL Being based on First-Order Logic, FLOWS makes use of logic predicates and terms to model the state of the world Features from situation calculus, like the use of uents, predicates, and terms which vary over time, were introduced to model the change of the world Invariant predicates and terms are called in relations in SWSO.

WAP 2.0 XHTML (MP)

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Suppose we have a spinner on a wheel labeled with all the numbers between 0 and 1 on its circular border The arrow is spun and the number the arrow stops at is the random variable, X The random variable X can now take on any value in the interval from 0 to 1 What shall we take as the probability distribution function, f (x) = P(X = x) We suppose the wheel is fair, that is, it is as likely to stop at any particular number as at any other The wheel is shown in Figure 81

Clearly, we can not make P(X = x) very large, since we have an in nity of values to use Suppose we let P(X = x) = 000000000000000000001 = 10 20 The problem with this is that it can be shown that the wheel contains more than 1020 points, so we have used up more than the total probability of 1! We are forced to conclude that P(X = x) = 0 Now suppose that the wheel is loaded so that P(X 1/2) = 3P(X 1/2), making it three times as likely that the arrow ends up in the left-hand half of the wheel as in the right-hand half of the wheel What is P(X = x) now Again, we conclude that P(X = x) = 0 It is curious that we cannot distinguish a loaded wheel from a fair one! The dif culty here lies not in the answer to our question, but in the question itself If we consider any random variable de ned on a continuous interval, then P(X = x) will always be 0 So we ask a different question, what is P(a x b) That is, what is the probability that the random variable is contained in an interval To make this meaningful, we de ne the probability density function, f (x), which has the following properties:

The FLOWS ontology consists of three major components: Service Descriptors, Process Model, and Grounding The Service Descriptors are used to provide basic descriptive information about the service The Process Model is used to describe how the service works The Grounding is used to link the semantic, abstract descriptions of the service provided in SWSO to detailed speci cations of messages, protocols, and so forth used by Web services In the rest of this section we take a closer look at the elements that are part of the FLOWS Service Descriptors (in Section 10411), and of the FLOWS Process Model (in Section 10412); we do not discuss the FLOWS Grounding in this section as we provide a wider overview of the general problem of SWS Grounding in Section 107 10411.

OS Version UI/Category Memory available to user Storage media Screen Pointing device Camera Symbian OS v7.0 UIQ 2.1 32 MB Yes; MMC and SD 208 320, 65536 colors TFT Yes Yes; 640 480 resolution GSM 900/1800/1900 HSCSD GPRS (4 + 2, class 10) MIDP 2.0 PersonalJava 1.1.1 Wireless Messaging Infrared Bluetooth USB

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