barcode vb.net 2010 Drawbacks to Pivoting in Software

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Drawbacks to Pivoting
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While a pivoted fact table may simplify the construction of certain forms of reports, it is not without its drawbacks. As with any form of derived schema, it behooves the user to choose the appropriate data structure to use for any given report. To avoid potential confusion, pivoted schemas are sometimes made available only to trained developers. In many cases, the additional effort required to develop and maintain a pivoted schema is not justifiable. In the example in Figure 14-2, the benefits of the transposed schema may seem trivial. The original schema is highly summarized, and there is only a need to transpose two transaction types. The relatively small effort required to do so within a report may not justify the additional burden of loading and maintaining an additional star. The value of a pivoted schema increases as the number of rows to be transposed into columns increases. If, for example, there were several dozen transaction types to be transposed, the value proposition changes significantly.
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The Sliced Fact Table
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A sliced fact table is exactly the same as the original star but only contains a subset of rows. Sliced fact tables are typically defined using a specific dimension attribute and may be useful in providing distributed applications, enforcing role-based security, or in reducing schema scope for use in an OLAP tool.
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Part V
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PART V
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Creating Slices of a Star
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A sliced fact table is derived from a star by selecting all rows that refer to a common dimension value. The resulting star has the same attributes as the original but may be significantly smaller. Additional slices may be created for each possible value of the dimension attribute. For example, the familiar star at the top of Figure 14-3 tracks orders by day, product, salesperson, and region. One of the attributes of the salesperson dimension table is region. This dimension takes on values such as East, West, and so forth. Order_facts contains orders for all regions.
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ORDER_ FACTS . . . order_dollars . . .
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CUSTOMER
PRODUCT
SALESREP . . . region . . .
Original Schema All regions Derived Schema Region-specific stars DAY ORDER_ FACTS_EAST . . . order_dollars . . . Region = East DAY ORDER_ FACTS_WEST . . . order_dollars . . . Region = West CUSTOMER CUSTOMER
PRODUCT
SALESREP
PRODUCT
SALESREP
Figure 14-3 Sliced fact tables
14 Derived Schemas 339
Regionally focused fact tables can be derived from order_facts. Each regionally focused star will contain all rows from the original fact table that are associated with a particular region. The lower part of Figure 14-3 shows sliced fact tables for the East and West regions. Although the diagram repeats each of the dimensions, it is not necessary to replicate these tables, unless the slicing is performed to support regionally distributed data. One set of unaltered dimension tables can be joined to the original order_facts table as well as each regional slice.
Uses for Sliced Fact Tables
Sliced fact tables serve handily in a variety of situations. The most common involves a regional or departmental focus, as in the example. Slices limit the size and scope of the data set without sacrificing detail. This makes them easier to distribute across physical locations, enables deployment in mobile applications, helps enforce security requirements, and permits generation of cubes of manageable size. Distributed solutions require replication of data to different platforms or locations. Where distributed systems have a regional focus, a sliced fact table reduces the amount of information that must be replicated, without sacrificing detail. By deploying sliced fact tables or cubes, you replicate all dimensions at each location, along with only the relevant subset of facts. Mobile solutions may be more severely limited by the size of the data set that can be carried offline. Sliced fact tables may be a convenient way to reduce the overall data set size, without sacrificing analytic detail. Another useful data structure for mobile solutions is the aggregate table, discussed in 15, Aggregates. Sliced fact tables offer a convenient mechanism for the enforcement of security requirements. Access to slices is granted to users based on their needs, ensuring that individuals only have access to the appropriate subsets of the data as called for by their job function. Table-level access is typically easier to configure and administer than row-based security schemes for the original schema. As previously noted, cubes do not always scale as well as relational data structures. Defining cubes as slices of the original data set is an effective way to limit their overall size, as are all of the other derivation techniques in this chapter, as well as the aggregation techniques in 15. NoTe The slicing technique is often referred to as horizontal partitioning of the fact table; the result contains all of the columns of the original fact table but only a subset of the original rows. Each partition, or slice, is defined by a dimension value. Related to the concept of horizontal partitioning is that of vertical partitioning. A vertical partition takes some subset of the attributes of the original data set, as opposed to a subset of rows. This may give rise to an aggregate table, as discussed in 15.
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