Data Mapping with XSLT3 Math Functions

XSLT3 adds trigonometry and other advanced math functions, new formatting functions, functions to collect environment variables, and more, extending XSLT and XSLT2 XML transformation standards. Data analysts and other data professionals can apply XSLT3 functions to solve XML data mapping and integration challenges that require complex mathematical computations. Let’s look at some MapForce examples of data mapping with XSLT3 math functions using trigonometry and other complex math expressions.

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New XML Grid and More in v2021r2

In the latest release of Altova desktop developer tools and server software products, we’re introducing a completely rebuilt XML Grid View, support for XSLT3 for XML data mapping, statistics and charts for monitoring FlowForce Server, and much more. Let’s take a look at the highlights of Altova Software Version 2021 Release 2. 

New features in Altova v2021r2
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Automatically Convert EDI to XML

Electronic Data Interchange (EDI) has proven to be a durable business-to-business communication technology in use today with history dating to the 1960s and even earlier. Efficient EDI data encoding reduced transaction payload size and improved data transfer speeds at a time when messages were sent over teletype at speeds equivalent to a 300 baud modem. EDI standards bodies promote wide acceptance among enterprises, and systematic evolution of new EDI formats extends support across industries. EDI improves profitability and is a dominant format for e-commerce data exchange.

But EDI files are barely human readable and need to be translated and mapped for compatibility with other business technologies. Altova MapForce is a graphical EDI mapping tool with native support for all major business data formats in use today, including XML, JSON, databases, flat files, Excel, and Web services, as well as the EDIFACT, X12, HL7, NCPDP SCRIPT, IDoc, PADIS, and SWIFT EDI transaction sets. MapForce can even automatically convert EDI to XML without the need to specify a target XML Schema or perform any manual mapping.

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Data Mapping Binary Objects – Part 2

Binary objects – BLOBs — can be cumbersome to manage in databases. In an earlier post we described a MapForce data mapping to insert binary objects into a database with generated metadata to identify the BLOBs later. The companion challenge in data mapping binary objects is to extract binary data and save it in a comprehensible form faithful to the original.

Let’s look at how that’s done.

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Data Mapping Binary Objects

Binary objects are difficult to manage in databases. They are large, their content is not human readable, and they can contain bytes of data easily misinterpreted as control characters. Even the data type name for binary large objects – BLOB – reflects most database managers’ dislike of them. Before relational databases, the definition of a blob was “something undefined or amorphous.”

Altova MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, includes features for effortlessly data mapping binary objects to or from all popular relational databases. Data such as images, PDF files, video files, or any other binary data can be mapped. Let’s look at an example.

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Validating Data Transformation

Software developers and other data professionals often need to transform data from one format to another. These transformations can be simple one-to-one conversions or may require more complex manipulation. For instance, relationships must be generated when importing flat CSV files into a database, or source data may need to be split for the target, as in full name vs. first, middle, last, and optional suffix. Validating data transformation is critical to prevent data loss or corruption.

In our recent post on Web service data integration combined a string value for GMT time with a numeric offset in seconds to generate the local time for weather forecasts. We created a user function that performed all the steps required to complete this operation. MapForce includes a powerful interactive data mapping debugger that can easily trace and validate this transformation. Let’s take a look at how it works.

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Web Service Data Integration

In a previous post we wrote that every data integration and reporting task needs to start with a clear understanding of the source data. Using grid view in XMLSpy, the industry-leading XML and JSON editor, we analyzed JSON data for 5-day weather forecasts retrieved from a Web service.

Continuing with our earlier scenario, we’ll use MapForce, the award-winning, graphical data mapping tool for any-to-any conversion and integration, to map the forecasts for a series of major cargo shipping ports into nicely formatted Excel documents. We’ll want to highlight any predicted high winds or heavy rainfall that could cause delays by interfering with cranes loading and unloading containers, or slowing ships entering and exiting the harbors.

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