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standing, and what seems initially like arcane and exotic knowledge often reveals itself as common sense. This thinking is a core principle behind the success of the pattern community. Discovering security architecture patterns is about developing such common sense. Security vendors in today s environment, because of their growing sophistication and maturity, can design and develop products at a production quality level above the reach of most applications. Buying off the shelf is more attractive than attempting an inhouse, homegrown solution. Vendor maturity also reduces costs as successful products reach economies of scale and competition keeps margins in check. This situation leads to the common problem of security architecture work being primarily integration work, where existing legacy systems have commercial security solutions grafted onto (or, if you prefer, finely crafted onto) existing insecure architectures. We will address the issue of security as an afterthought in detail. In the preface, we described some of the advantages that vendors had over projects, including better knowledge of security, biased feature presentation with emphasis on the good while hiding the bad, and deflection of valid product criticisms as external flaws in the application. On the other side, vendors of security products often share common flaws. We will introduce three in the following discussion and expand on their resolution in the chapters to follow.

Dreamweaver gives you two ways to handle code completion. The first option, After Typing </ works by inserting the closing tag after you enter the first two characters. This has become my preferred technique because it allows me to enter the opening tag, the enclosed code, and then to close it with just two characters. If you prefer the legacy method, choose the After Typing The Open Tag s > option. With this option selected, after I type <b> into Code view, the corresponding </b> will be added as soon as I type the last > in the bold tag.

else { //set oid parm to value set by Find CeWriteRecordProps(globalHDB, globalRecOID, NUMBER_BIRTHDAY_PROPS, pRecord); }

Abigail then computes Invita s expected losses over the next five years as follows: (Cost of catastrophe) (Probability of catastrophe in five years)

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All text is entered in the Set Text of Status Bar parameter form (see Figure 19-21) in the Message text box.

, Int Ed 70(11),89 (1991) 290 R S Logan and R L Banks, Hydrocarbon Process 47(6), 135 (1968); Oil Gas J 66(21),131 (1968) 291 Hydrocarbon Process 47(9), 165 (1968) 292 L F Albright, Oil Gas J 88(48), 70 (1990) 293 L F Albright, M A Spalding, J Faunce, and R E Eckert, Ind Eng Chem Res 27, 391 (1988) 294 C L Rogers, Oil Gas J 69(45), 60 (1971) 295 A E Eleazar, R M Heck, and M P Witt, Hydrocarbon Process 58(5), 112 (1979) 296 R M Heck, G R Patel, W S Breyer, and D D Merrill, Oil Gas J 81(3),103 (1983) 297 S Novalany and R G McClung, Hydrocarbon Process, Int Ed 68(9),66 (1989) 298 G Chaput, J Laurent, J P Boitiaux, J Cosyns, and P Sarrazin, Hydrocarbon Process, Int Ed 71(9), 51 (1992) 299 K R Masters and E A Prohaska, Hydrocarbon Process.

circumstance, the gain criterion values remain unchanged for attributes light and ground condition , and the attribute weather is still chosen as the rst split in the tree. However, its in uence has been reduced (from 0.5 using gain criterion to 0.33 using gain ratio) so the effect on the gain criterion exerted by the fact that the attribute splits the data into more subsets has been reduced somewhat by using the gain ratio. By using a simple algorithm and an information-theory approach, the identi cation tree can discover useful and accurate information from a set of data which may not be obvious on rst observation. The small example dataset, when combined with the ability to process continuous values and a revised gain ratio, illustrates the principles behind the execution of the identi cation tree algorithm. These improvements to the algorithm become increasingly valuable when there are thousands or millions of data records in the dataset. With data of this size and type, which is frequently the norm in commercial and scienti c applications, a robust, ef cient and accurate algorithm is necessary to extract meaning from the collected data. The entire process of data collection, manipulation, knowledge discovery and interpretation is known as data mining and probably constitutes the single largest application of arti cial intelligence techniques outside the academic laboratory. The identi cation tree algorithm is certainly not the only data mining algorithm, but it is one of the most popular. There are, however, some drawbacks to the approach.

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