barcode for excel 2010 Profiles in Software

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Table Profiles
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A table profile summarizes a table as a whole, the columns within a table, and the relation ships between tables as shown in Table 82 Because table profiles are tedious to construct manually, most DBMSs provide statistics programs to construct them automatically The
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Relational Database Design
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TABLE 82 Typical Components of a Table Profile
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Number of rows and physical records Number of unique values, distribution of values, correlation among columns Distribution of the number of related rows
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FIGURE 84
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9000 -r 8000 -7000 -s 6000 -o *o 5000 -4000 -E 3000 -2000 -1000 -0 -I
Example Equal-Width Histogram for the Salary Column
Salary histogram (equal width)
1000050000 5000190000
I 1 1 1 1 1 1 90001- 130001- 170001- 210001- 250001- 290001- 330001- 370001130000 170000 210000 250000 290000 330000 370000 410000 Salary
designer may n e e d to periodically run the statistics program so that the profiles do not b e c o m e obsolete For large databases, table profiles may be estimated on samples o f the database U s i n g the entire database can be too time-consuming and disruptive For column and relationship summaries, the distribution conveys the number o f rows and related rows for column values The distribution o f values can be specified in a number o f ways A simple way is to assume that the column values are uniformly distributed Uni form distribution means that each value has an equal number o f rows If the uniform value assumption is made, only the minimum and m a x i m u m values are necessary A more detailed way to specify a distribution is to use a histogram A histogram is a twodimensional graph in which the x-axis represents column ranges and the y - a x i s represents the number o f rows For example, the first bar in Figure 84 means that 9,000 rows have a salary between $ 1 0 , 0 0 0 and $ 5 0 , 0 0 0 Traditional equal-width histograms do not work well with skewed data because a large number o f ranges are necessary to control estimation er rors In Figure 84, estimating the number o f employee rows using the first two ranges may lead to large estimation errors because more than 97 percent o f employees have salaries less than $ 8 0 , 0 0 0 For example, y o u would calculate about 1,125 rows (125 percent o f 9,000) to estimate the number o f employees earning between $ 1 0 , 0 0 0 and $15j000 using Figure 84 However, the actual number o f rows is much lower because few employees earn less than $ 1 5 , 0 0 0 Because skewed data can lead to poor estimates using traditional (equal-width) his tograms, most D B M S s use equal-height histograms as shown in Figure 85 In an equalheight histogram, the ranges are determined so that each range has about the same number o f rows Thus the width o f the ranges varies, but the height is about the same Most D B M S s use equal-height histograms because the m a x i m u m and expected estimation errors can be controlled by increasing the number o f ranges
8 Physical Database Design 255
FIGURE 85
5000 4500 + 4000 3500 3000 I 2500 1 2000
Example Equal-Height Histogram for the Salary Column
Salary histogram (equal height)
1500 + 1000 500 0 1200021400 2140127054 2705532350 3235135600 35601- 3903339032 42500 Salary 4250149010 4901158100 5810167044 67045410000
TABLE 83 , Typical Components
of an Application Profile
, Application Type
Statistics
Query Form Report
Frequency, distribution of parameter values Frequency of insert, update, delete, and retrieval operations Frequency, distribution of parameter values
Table profiles are used to estimate the combined measure o f performance presented in Section 812 For example, the number o f physical records is used to calculate the physi cal record accesses to retrieve all rows o f a table The distribution o f column values is needed to estimate the fraction o f rows that satisfy a condition in a query For example, to estimate the fraction o f rows that satisfy the condition, Salary > 45000, y o u would sum the number o f rows in the first three bars o f Figure 84 and use linear interpolation in the fourth bar It is sometimes useful to store more detailed data about columns If columns are related, errors can be made w h e n estimating the fraction o f rows that satisfy conditions connected by logical operators For example, i f the salary and a g e columns are related, the fraction o f rows satisfying the B o o l e a n expression, Salary > 45000 AND Age < 25, cannot be accu rately estimated by knowing the distribution o f salary and age alone Data about the statis tical relationship between salary and age are also necessary Because summaries about column relationships are costly to collect and store, many D B M S s assume that columns are independent
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