barcode vb.net 2010 Part IV in Software

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Part IV
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PART IV
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Fact Table Design
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Summary
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Type-specific facts and dimension attributes complicate schema designs. Three primary mechanisms exist for coping with these heterogeneous attributes. A single dimension table can be designed to include all possible type-specific attributes. The same can be done in the fact table for type-specific facts. This approach may leave a lot of columns empty and can lead to nonsensical reports, but it is relatively simple to design and build. It may not be feasible if the number of possible attributes is very large. A core and custom approach creates a single dimension table for all common attributes, and additional type-specific dimension tables for each type. The same can be done in the fact table for type-specific facts. The custom tables also include core attributes, in order to simplify analysis. The common attributes of the core and custom tables must conform in structure and content. Type-specific attributes can be captured in generic, multipurpose columns. This makes the data more difficult to report on and is most effective when used with a specially designed front-end application.
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Further Reading
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Heterogeneous type-specific attributes are a common occurrence. You can probably visualize several examples on your own. If you would like to read more about them, here are some places to look. Ralph Kimball and Margy Ross describe the use of core and custom tables in the financial industry. Their example deals with the different dimensions and facts associated with different account types, such as checking accounts, savings accounts, and mortgage accounts. It appears in 9 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). In that same chapter, Kimball and Ross describe another situation in which this technique may be useful: sales of products and services. They also point out that heterogeneous attributes are often present in the insurance industry; see 15 of The Data Warehouse Toolkit. This chapter mentioned an implementation alternative involving the placement of custom attributes into outriggers. This technique avoided the duplication of core attributes, but risks defeating advanced DBMS query optimizers. Kimball and Ross call these context-dependent dimension outriggers. For an illustration, see 9 of The Data Warehouse Toolkit. The process of designing and building aggregate tables must be carefully thought out when there are core and custom stars. Aggregates can focus on core attributes, but there may also be room for type-specific aggregates. These possibilities are discussed in 8 of Mastering Data Warehouse Aggregates by Chris Adamson (Wiley, 2006).
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ChAPTeR 14
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Derived Schemas
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PART
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Performance
ChAPTeR 15
Aggregates
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32 14
ChAPTeR
Derived Schemas
Performance is a guiding principle of dimensional design, as you learned in 1. It is also one of the foundational principles of data warehousing in general. By restructuring data at load time, rather than query time, answering analytic questions about business processes is faster and easier. Sometimes, however, faster is not enough. While a well-designed schema can deliver answers to most queries with reasonable rapidity, some queries will require more complex processing. As data sets grow large, even simple queries may exhibit degraded performance. Luckily, there are solutions to these problems. In this chapter, you will learn how derived tables take information from an existing dimensional schema and restructure it for special purposes. In 15, you will learn how aggregate tables are used to pre-summarize information in large data sets. Both techniques can dramatically boost the performance of a dimensional design, without requiring investment in proprietary hardware or software solutions. Derived schemas store copies of existing dimensional data that has been restructured. These data structures can improve query performance and reduce report development complexity, at the cost of additional work loading and managing them. This chapter highlights some derived structures that have already been encountered, and illustrates four more: Merged fact tables precompute drill-across results, making it easier and faster to compare facts from different fact tables. Pivoted fact tables transpose row-wise data into column-wise data, or vice versa, simplifying the construction process for some kinds of reports. Sliced fact tables contain a subset of rows of the original fact table. They are useful for distributed, departmental, and mobile applications, and may also be used to enforce role-based security. Set operation fact tables store results from comparing two stars with union, intersect, or minus operations, dramatically improving the performance and complexity of reports that require these comparisons.
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