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While the approach taken by auto1.fps of placing expert knowledge in rules works, the resulting program is not easy to maintain; any change or addition requires modifying the program. The number of rules can be fairly large; one for each node in the decision tree plus three additional housekeeping rules, adding up to 14 rules in this very simple program. We now consider an alternate approach; placing expert knowledge in a database, and writing rules to interpret that database. This approach, used in auto2.fps, gives a ve-rule program that is independent of the size of the decision tree. If we had a thousand nodes in our decision tree, we would still have only ve rules. Our decision tree structure remains that of the hypotheses in Table 1; however, the multiple rules are different. In auto2.fps, we have only one hypothesis-verifying rule; r0. The other rules are housekeeping rules, to ensure that the rules are red in the proper order and to tell the user what is going on. 12.4.1 Expert Knowledge Database Structure



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There s a simple way to figure this out, though it requires you take into account not only the initial group of subscribers who sign up but never pay or listen, but also the monthly loss of subscribers due to normal attrition the churn rate You will see that we re using two churn rates to calculate the total number of subscribers at the end of each month of the year, the 15-percent rate representing the fall-off in new subscribers after the first month (we discount the total number of new subscribers by 15 percent by multiplying the total by 085) and a monthly churn rate of 5 percent In other words, you ll probably lose about 5 percent of all subscribers every month These percentages represent a kind of best-case scenario.





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to ask, in order to receive a sequence of af rmative answers: Was his tie loose Was his top shirt button undone Was he laughing Did he begin singing Was he loud Was he walking slowly Was his route indirect Was his gait unsteady The witness may still answer, in the negative, the nal (dichotomous) question: Was he drunk, yes or no But at this stage it matters little. The picture painted, by the af rmative answers, produces a suf cient albeit ambiguous if the jury cared to think about it closely picture of a drunken man. Indeed a No answer to the nal question whether he was drunk could be turned into an advantage. It would appear to contradict the earlier answers and so discredit the witness. The skilful lawyer asks questions in a particular sequence. The object of the sequence only becomes evident at the end. By the time that the nal question is reached it is very dif cult for the witness to resist the inferences of his or her previous answers.

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RM is called finer than RN , written RM RN , iff (RM RN ) (RN RM ). A decomposition RM is called minimal iff it is irredundant and there is no irredundant decomposition that is finer than RM . Clearly, we do not want redundant decompositions. If a set M1 M 2U is a subset of another set M2 M, then cextU 1 (projU 1 (RU )) cextU 2 (projU 2 (RU )) = cextU 2 (projU 2 (RU )), M M M M M M so we can remove the projection to the set M1 without destroying the decomposition property. The notions of a decomposition being finer than another and of a decomposition being minimal serve the purpose to make the sets of attributes defining the decomposition as small as possible. If there are two irredundant decompositions RM and RN of a relation RU with M M, N N and M {M} = N {N} and M N, then obviously RM RN . Hence in minimal decompositions the sets of attributes underlying the projections are as small as possible. It would be convenient if there was always a unique minimal decomposition, because then we could always find a single best decomposition. However, in general there can be several minimal decompositions. This is demonstrated in Figure 3.10, which shows a very simple relation RABC over three binary attributes A, B, and C. As can easily be seen from the projections also shown in Figure 3.10, RABC can be decomposed into {RAB , RBC }, into {RAB , RAC }, or into {RAC , RBC }, all of which are minimal.

You may have a perfectly viable business and remember, for purposes of this discussion we are assuming you want to build a business, while many people just want to podcast for fun with higher churn rates, but you ll need to adjust your ad or subscription rates higher to account for the extra churn If you are looking for a very targeted audience, a high churn rate may be exactly what you want to make sure that you are arriving at the audience that is really interested in the topic of your show and what your advertisers are selling..

An alternative method is to use daytime as the packet sequence. In this way the source node does not have to store the sequence number. However, this method is sensitive to clock adjustment at the source node.

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Note the use of the matrix construction function in the example above. It is this that gives the interpretation of the operation as a matrix product. Multiplying two arrays, on the other hand, gives the product element-wise, e.g.

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