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proceed: go to the left subtree, if not terminal block then proceed, else process the block, go up to the root for this subtree, if there is a record, then do; process the record, go to the next subtree, proceed, end; else do; if this is the root for the entire file, then done, else proceed, end; done: A tree-structured le will be balanced if the number of levels is equal or nearly equal for all branches of the tree A balanced tree will require on the average fewer block accesses to locate a record, given that record requests are uniformly distributed 4-4-1 Evaluation of a Tree-Structured File The major advantage of this structure is the nonredundancy of storage The goal record is directly associated with its key Key elds appear only once, they are not replicated at other levels In the evaluation which follows we assume that the records are densely packed into blocks, although not spanned Update performance would be better if a nondense B-tree scheme were used, but the reduction in density reduces the e ectiveness of the tree structure We now nd goal records throughout the levels of this le and at di erent seek distances from the root index A disadvantage of the tree-structured le is that the

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fanout can be signi cantly reduced versus the fanout of an index so that many more seeks may be required to retrieve an average record of a large le The increase in the expected number of block retrievals depends on the ratio of key sizes to goal sizes, which controls the reduction of the fanout factor Some access bene t is gained because some records (7 out of 26 in Fig 4-20) are closer to the root and hence easier to retrieve This factor will only outweigh the reduction in fanout if the goal records are extremely small This happens when the tree is used as an index tree but then another access is required to access the data le An operational bene t of trees is that keys are stored in only one place, and hence are easier to update Records can be of variable length, since the access algorithms do not depend on a speci c number of records per block In order to simplify the analysis, a xed value for R will be used below, which will provide adequate results if there are many records per block (R B) The fanout ratio is given by y= B P R+P +1 4-10

found by inspection of Fig 4-19 Even though the lowest level does not require further pointers, space for such pointers is frequently allocated, since this simpli es further growth of the le Then the fanout ratio y is the same at level 1 We assume now that the le is lled at the top levels (x, , 2), rather than at level 1 as shown in Fig 4-20, and hence is balanced This will provide the shortest access paths to the data The number of records at each level is again determined by the number of blocks on a level and by the fanout ratio Note that here, as with the original B-tree, the number of records in a block is one less than the fanout nlevel = (y 1)blevel = (y 1)y x level If the tree is balanced, only the bottom level (1) will be partially lled; it will contain the remaining records, computed by subtracting the records on levels x 2 from the total le size n

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Once you ve calculated EP% for your business units or segments, it helps to plot the results on a chart like that shown in Figure 62 The height and direction of each bar represents the amount (in percentage) and direction of EP generation (positive is above the midline; negative below) The width of the bars represents the amount of capital tied up in that business unit The arrangement in order from largest positive EP to largest negative EP generates the waterfall pattern that gives this chart its nickname

n1 = n (y 1)

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