barcode vb.net 2010 Multiple Changes on a Single Day in Software

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Multiple Changes on a Single Day
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The policy example only allows for one version of a policy on a given day. If it is necessary to track changes at a finer grain, this can be achieved by adding columns to capture the time of day at which the record became effective and expired: effective_time and expiration_time. Depending on the business case, these columns may represent hours (24 possible values), minutes (1,440 values), or seconds (8,640 possible values). Together with the effective_date and expiration_date, they allow for multiple versions within a given day. As with the day columns in the prior example, the time columns must allow for no overlap and leave no gaps. For policy changes recorded at the minute level, for example, if one row becomes effective at 12:48 PM, the prior row must expire at 12:47 PM.
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With the time columns in place, it is possible to have multiple versions of a policy on a given day. This means that qualifying a query for a particular date may pick up multiple versions of the same policy. This is not desirable if, for example, you are counting the number of covered parties across all policies on a particular date. You must now supplement the date qualifications with time qualifications, as in:
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WHERE 12/31/2006 >= effective_date AND 12/31/2006 <= expiration_date AND 24:00 >= effective_time AND 24:00 <= expiration_time
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You can add a last_change_of_day flag to simplify this kind of query. It will be set to Final for the last change to a given policy on a day. End-of-day status for a particular date can now be captured by using this SQL:
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WHERE 12/31/2006 >= effective_date AND 12/31/2006 <= expiration_date AND last_change_of_day = "Final"
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Time-Stamped Dimensions and the ETL Process
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Construction of a time-stamped dimension places an extra burden on the developers of the process that loads and maintains the dimension table. When a type 2 change occurs, it will be necessary to identify the prior row to update its expiration_date and most_recent_version columns. Extract, transform, load (ETL) developers may also be charged with grouping a set of changes that occur on a single day into a single time-stamped dimension record. This additional work is not trivial, but ETL developers will find benefits in other areas. When loading transactions into a fact table, such as Figure 8-1 s policy_payment_facts, the ETL process must determine what foreign key to use for each dimension. If a payment is recorded for policy 40111, for example, which policy_key is used in the fact table Without time stamps, it would be necessary to compare the policy characteristics in the source system with the type 2 attributes in the dimension table. If a payment comes in for policy 40111, with a family size of four and a deductible amount of 500, for example, then surrogate key 14922 should be used. This lookup process is explored in greater detail in 17, Design and ETL. When dimension rows are time-stamped, this work is greatly simplified. To choose the correct surrogate key value, the ETL process need only know the natural key and the date of the transaction. This date can be compared to the effective_date and expiration_date columns in the dimension table to identify the correct key value for use in the fact table. This characteristic of a time-stamped dimension is particularly valuable when fact tables are implemented incrementally. Suppose, for example, that a year after the policy payment star is implemented, another star is added to track claims against policies. Like the policy payments star, policy_claim_facts will involve the policy dimension table. If each row in the
8 More Slow Change Techniques 179
policy dimension has been time-stamped, it will be easy to load the last year s worth of claims into the new fact table. For each historic claim, the date of the transaction is used to single out the appropriate row in the policy table. Without these time stamps, it would be necessary for the source system to supply not only the date of each claim but also a complete picture of the policy, including the policy_number and each of the type 2 attributes. This set of values would be used to identify the associated row in the policy table. TIP For dimension tables that represent closely watched entities, supplement type 2 changes tracking with date stamps. This will allow for streamlined ETL processing, and will greatly simplify the load process if an additional fact table is added in the future. It is not necessary to time-stamp every dimension. The additional work may not be necessary if point-in-time analysis is not required. Dimensions that contain reference data, such as the transaction_type table in Figure 8-2, do not require time stamps. For core dimensions that conform across multiple subject areas, however, time-stamping is a useful enhancement. As with any other dimension table, the time-stamped dimension can be joined to a fact table to analyze facts. The time-stamped policy table from Figure 8-3, for example, can be joined to a policy_payment_facts table to study payments in the usual way. Need to know what were the total January payments on policies covering two or more people Join the policy dimension to the fact table and constrain on covered_parties. Want to compare the payment habits of single versus married policy holders Group the query results by marital_ status. When analyzing the dimension table without a fact table, something interesting happens. Some of the dimension attributes take on the characteristics of facts, exhibiting the property of additivity. For example, the following query captures the number of covered parties by state on December 31, 2006:
SELECT state, sum(covered_parties) FROM policy WHERE 12/31/2006 >= effective_date AND 12/31/2006 <= expiration_date GROUP BY state
In this query, the dimension column covered_parties is behaving as a fact. Some people like to call it a hybrid attribute since it can be used either as a dimension or as a fact. This is a common phenomenon in time-stamped dimensions.
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