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keeping all contract information, the user support component is responsible for making sure that those contracts are kept safely. On the one hand, this means that it blocks any user requests that are not covered by the contract the customer, who pays for the user, has. On the other hand, is can make sure that a service requested by a user is delivered, if the contract allows it. 4.7.4.3 Open ICCAS Interfaces All ICCAS interfaces between the components just described are designed to act (1) as protocols, allowing for open communication between two remote entities of components, or (2) as software interfaces, reflecting the clear architectural decision, that the interaction between those components happens within a common address space [120]. The QoS Interface (CPP) offers the control of routers QoS components, where services to the customer are provided. It can be used to set QoS parameters of routers, depending on the technology in place. The Mediation Interface (AIP) is responsible for collecting data from several mediation entities, possibly even from mediation entities of different types. The usage data, which are mediated after the data gathering takes place, need to be transferred to the ICCAS. Therefore, a protocol is designed that defines rules and transmission units for transferring mediated data to the accounting component. Since the anticipated load for this interface will be high, the protocol must be highly efficient, yet extensible. The data exchanged across this interface include one of the following alternatives, which depend on the particular scenario: (1) a simple handover of data gathered by metering, or (2) a handover of data mediated based on the particular inputs from the

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C[cnt][nodeCnt].shortRate = r; r = node_ptr->shortRate; C[cnt][nodeCnt].cashFlow = swapRate*principalAmount (C[cnt-1][nodeCnt-nodeCnt2].shortRate 1)*principalAmount; C[cnt][nodeCnt].moneyMarketAccount = (C[cnt-1][nodeCnt-nodeCnt2].moneyMarketAccount)*(node_ptr->shortRate); if (i == N) C[cnt][nodeCnt].zeroCouponBond = new_ptr->bondPrice[N]; } } // compute swap values if (cnt > 1) C[cnt][nodeCnt].swapValue = principalAmount*(1 + swapRate)*(curr_ptr->bondPrice[cnt+1]) principal Amount; else if (cnt == 1) C[cnt][nodeCnt].swapValue = principalAmount*swapRate*(curr_ptr->bond Price[cnt+1]) + principalAmount*(1 + swapRate)*(curr_ptr->bond Price[cnt+2]) principalAmount; else C[cnt][nodeCnt].swapValue = 0.0; } if (nodeCnt != 1) { nodeCnt--; if (nodeCnt % 2 != 0) nodeCnt2--; } else { nodeCnt = pow(2, cnt+1); nodeCnt2 = pow(2, cnt); }

We are interested in actually implementing the model in equation (1.18) using a Monte Carlo simulation. Suppose we wish to simulate a sample path of geometric Brownian motion process for the stock price. We divide the time interval T t into

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Associations, see Data mining tasks, associations Associative rules, 13, 17, 169, 173, 220, 253, 257 258 consequence, 258 lift, 258 support, 258 Assumptions, 4 Atoms, 203 205 Attrition, 167 168 AUC, see Area under the curve Average linkage, see Agglomerative hierarchical clustering, average linkage Average, see Mean Background colors, see Graphics design, use of color in Backward selection, 144 Bagging, 163 Balanced sampling, 10, 113 Banking, in graphs, 30 Bar chart, 5, 36 Bayes theorem, 157 158 Beta coef cients, see Logistic regression, beta coef cients; Multiple linear regression, beta coef cients Binary distance, see Distance, binary Binning, 199. See also Transformation, creating bins Bioinformatics, 178, 214 Biological target, 177 Biosequences, 177 178 Bivariate data, see Data, bivariate Bivariate data visualization, 49 50. See also Data, bivariate scatterplot, see Scatterplot uses for, 49 Bold colors, see Graphics design, use of color in Bonds, 203 206 Boosting, 163 Bootstrapping, 163 Box plot, 5 6, 14, 41 43, 219, 242 243, 245 246 cutoffs, 43 elements of, 41 Box Cox, 8, 232 Brushing, 5, 19, 64, 66, 223, 242 246 Budget, 2 Build, see Model, build

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