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adding value as every phone added value to the growth of the telecommunications industry, as every web site added value to the growth of the Internet, and we will harvest the true value of this networked world only when we begin to manage the people and the technology in a systemic (unified) way. This book presents several ways to do just that.

Internet Radio Station (Icecast)

where k is Boltzmann's constant (1.38 x 10 23 Joule/Kelvin). T must be expressed in absolute degrees i.e. Kelvin (K) (temperature expressed in Kelvin = temperature expressed in Celsius degrees + 273).

s I said in 1, Web 4.0 is right around the corner. The rst phase, Web 1.0, ran from about 1989 to 1995, which was the time of web site building using HTML. The second phase, Web 2.0, started with the advent of the browser. Netscape, Internet Explorer, and other browsers enabled people to maneuver around the Web, search more effectively, and do e-commerce more ef ciently. We saw the rise of Yahoo! and Google. Dot-com companies were hot, hot, hot. As Web 2.0 started to mature, and before the dot-com bubble burst, companies had planted the seeds for the social web. Think Amazon, think eBay both of which promoted a social structure 219

S5.364(up) S5.365(d)

When volatility is stochastic, it is not possible to maintain a perfect hedge. Errors are introduced by the fact that the actual (process) volatility is unknown. Thus, if the implied (hedging) volatility is not equal to the process volatility, there will be a standard error of the portfolio value. This standard error increases with the volatility error since the variance of the portfolio value is proportional to the variance of the volatility forecast. Suppose we want to price an option f that pays off F(ST ) at expiration. We will try to hedge it by trading both the underlying asset and another option g that pays off G(S*) at its expiration T*. We will construct a dynamic self- nancing replicatT ing strategy that exactly replicates the option f. We construct the portfolio = S + hg NA f That is, we sell one option f and hedge with shares of S and h options g, and nance by selling short N shares of the money market account, effectively borrowing at the risk-free rate. Assuming our strategy is self- nancing so we don t have to differentiate the weights, the change in the portfolio value over dt is d = dS + hdg NdA df

Often, data mining projects involve a data set with a large number of continuous variables. If a data set has too many variables, it can be dif cult to understand. In addition, the use of all variables in any analysis may introduce a host of logistical and accuracy problems. For example, using a large number of variables may increase the time to compute a particular model beyond an acceptable threshold. Using too many independent variables in a particular model may also impair the accuracy or reliability of a model by over tting the data. There may even be logistical problems in collecting the values of many variables when the model is deployed. Principal component analysis provides a method for understanding the meaning of a data set by extracting a smaller series of important components that account for the variability in the data. Each of these factors or principal components considers a subset of the variables to be important. For example, a data set containing information on home loans may contain a host of information about an individual, such as salary information, current home price, credit card debt, credit score, and so on. These variables could then be analyzed using principal component analysis. The analysis may group the variables in a number of different ways. For example, variables such as current home price and salary information may be grouped together in a larger group indicating wealth indicators, whereas credit card debt and credit score may be grouped together as credit rating factors. The use of principal component analysis offers the following bene ts: