barcode vb.net 2010 Semi-Additivity in Software

Generation Quick Response Code in Software Semi-Additivity

Semi-Additivity
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The status measurement collected in a snapshot fact table is usually semi-additive. Unlike the additive facts in a transaction fact table, the semi-additive fact cannot be summed meaningfully across the time dimension. This does not mean it cannot be aggregated across time; averages, minimums, and maximums may all be of use. Computation of an average, however, may require care. The concept of semi-additivity is, sadly, familiar to anyone who owns a bank account. If there is $100 in your account at the end of each day of the week, that does not mean you have $700 at the end of the week. Unfortunately, your account balance is not additive across time. The balance is additive across other dimensions, however. On any given day, it makes perfect sense to add balances together from more than one account, such as your checking and savings accounts. A bank may add balances together for all accounts at a branch or for account holders with particular demographics. As long as the fact is not summed across multiple snapshot periods, it remains additive. While it cannot be summed, a semi-additive fact may be meaningfully summarized across the problem dimension in other ways. Possibilities include computing the minimum, maximum, and average values. With a savings account, for example, the amount of interest paid each month is often computed using an average daily balance. Fees are often assessed based on the account balance falling below a minimum level during a particular period. Both these measurements summarize a balance across multiple snapshot periods. TIP In a periodic snapshot, there is usually at least one semi-additive fact that cannot be summed across snapshot periods. This semi-additive fact can be summarized across periods in other ways, including the computation of minimum, maximum, and average values. In every query and report, semi-additive facts must be used carefully. When summing the semi-additive fact, the query must be constrained by a unique row in the nonadditive dimension, or grouped by rows in the nonadditive dimension. Queries that sum account balances, for example, must either filter for a specific date or group the results by date. If the report contains subtotals or grand totals, the same rules must also be followed within the report itself do not sum across the nonadditive dimension when creating a total. The restrictions on the use of semi-additive facts can prove problematic for business intelligence software packages that facilitate ad hoc query construction. Generally, there is little that can be done to prevent users from summing the semi-additive fact over the problem dimension. Some tools provide the capability to force a constraint into each query, and this capability can be leveraged to force the user to constrain for a particular snapshot period. This technique has its drawbacks. Although safer, it limits the reports that can be produced to those focused on a single period. Experienced developers may be provided with a separate metadata layer that does not enforce this restriction, allowing them to build reports that group data by periods or construct averages across periods. These techniques are further discussed in 16.
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11 Transactions, Snapshots, and Accumulating Snapshots 269
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The SQL AVG() Function
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Special care must be taken when averaging values from a periodic snapshot. Sometimes, the SQL AVG() function will provide inaccurate results. The SQL average function works by summing a series of values and then dividing by the number of rows. In the case of the banking example, this allows the computation of a period average. The grain of the star guarantees that there will be one row for each snapshot period. A snapshot design for store inventory, however, may record rows only for in-stock products. This technique avoids recording rows with zero quantity on hand, which would clutter up reports. If you are averaging the quantity on hand of a particular product during a particular month, it is necessary to sum the snapshot values and then divide by the number of days in the period, rather than rely on SQL s AVG() function. Always consult the grain of the snapshot fact table before trusting in the SQL AVG() function to construct a period average.
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You may have noticed that the last few paragraphs refer to the nonadditive dimension or the problem dimension. For a balance or level in a periodic snapshot, the problem dimension is always the snapshot s period. There are cases, however, where a semi-additive fact s nonadditive dimension is something other than time. An example has already been encountered in this book. The sales_goal star from Figure 5-3 contained a fact called goal_ dollars. This fact can be aggregated across months, to compute the total goal over time, or across territories, to compute goals across geographies, but it cannot be aggregated across versions of the sales plan. If two different plans set sales goals for July 2010, these two goal amounts cannot be meaningfully added. Similarly, in a financial system that supports budgeting, there are likely to be multiple versions of the budget. Amount budgeted cannot be aggregated across plan versions. As already mentioned, some status measurements are not additive at all. Common examples include reservoir water levels, ambient temperature, or the internal pressure of a machine or device. These measurements may be valuable, however, if sampled on a periodic basis in a snapshot. While they cannot be added across any dimension, they can be studied in other ways that may provide valuable process insight, including the computation of minimums, maximums, or averages.
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