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can be reused on any tier in a multi-tiered client/server environment, offering an added level of reuse for XML documents. The same cannot be said of scripting environments or platform-specific binary executables.

do with reports that include two or more dimension tables but involve no facts This kind of query is sometimes called a cross-browse Unlike a standard browse query, a cross-browse requires going through a fact table to link the requested dimensions The difficulty is that when there are multiple stars, there may be more than one way to relate two dimensions Query generators don t do well in this situation, since a list of data elements requested by the user is not enough to choose a join path In Figure 16-3, for example, products and days can be related in the context of orders or shipments If a user wants a list of products that were ordered on January 8, choosing date and product does not provide enough information for the SQL generator It must be able to determine whether to relate day and product via order_facts or via shipment_facts.

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Given the industry s vast knowledge of communications, networking, and data processing, validating and processing data from other departments and/or enterprises should be a simple task. Unfortunately, that s not the case. Validating data formats and ensuring content correctness are still major hurdles to achieving simple, automated exchanges of data.

In this case, the GUID type is defined as a set of 8, 4, 4, 4, and 12 hexadecimal digits divided by dashes (-). The W3C Recommendation defines the following facets: length minLength maxLength pattern enumeration whiteSpace maxInclusive minInclusive maxExclusive minExclusive totalDigits fractionDigits Naturally, you cannot use every facet with every type. Only some combinations make sense.

Using XML technology as the format for data exchange can quickly remedy most of these problems for the following reasons:

A query like this cannot be formulated unless the tool has some mechanism for asking users how they want to relate products and days Some tools have an option that can be configured to ask the user in a situation like this This obstacle can be overcome by creating a separate semantic layer for each star one for orders and one for shipments In essence, choosing which star to work with answers the question even before the report is formulated: I am working with orders I want to see a list of products by day Since each star is now isolated, a merged fact table will once again be required for drilling across TIP When multiple stars share two or more dimensions, cross-browse queries can trip up SQL generators If these queries are common, separate semantic layers for each star may be necessary.

build proprietary parsers for each data format. XML technology eliminates this requirement by using a standard XML parser.

The same effect can be achieved by making heavy use of views or aliasing within a single semantic layer For example, creating separate views of the product, day, and customer table for use with orders and with shipments allows each star to have its own virtual replica of the shared dimensions This may make for a cluttered semantic layer, since it will have to distinguish between the versions of each dimension attribute for example, ordered_ product_name vs shipped_product_name When the model includes a factless fact table, there are likely to be numerous queries that do not specify a fact For example, the orders star may be supplemented with another that specifies products that are on promotion When a user selects product and day, it is not sufficient to assume that the factless fact table be used.

that all the required fields are provided and are in the correct order. This function, however, requires the availability of a DTD. Additional content validation is possible by developing applications using the W3C DOM. In addition, content and format validation can be completed outside of the processing application and perhaps even on a different machine. The effect of this approach is twofold: It reduces the resources used on the processing machine and speeds up the processing application s overall throughput because it does not need to first validate the data. In addition, the approach offers companies the opportunity to accept or deny the data at time of receipt instead of requiring them to handle exceptions during processing.

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