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EDI Data Mapping with MapForce


Any computer industry standard that promotes reliability and clear communication between independent enterprises will have a long life. EDI (Electronic Data Interchange) originated in the 1960s to enable automated transactions between corporate computer systems. EDI remains in widespread use today and continues to evolve to meet modern requirements, under the administration of the UN/EDIFACT and ANSI standards bodies.

Altova MapForce supports EDI data mapping between EDI messages and XML, JSON, relational databases, flat files, Excel, or other data formats to bridge between commonly used information interchange and in-house technologies.

MapForce includes support for the latest EDIFACT versions 2015B and 2016A including the new VERMAS message. Mapping and translating EDIFACT messages to other usable data types for transfer, storage, and management is a common business requirement solved by MapForce.

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MapForce Offers Dynamic Access to Node Names


There are situations, especially when encountering loosely structured data, where you may want to map and transform structural components of a data stream along with content. MapForce 2017 includes a new feature to dynamically access node names of XML elements, attributes, or text file columns such as the contents of CSV files, to target components.

Dynamic access to node names allows creation on the fly of target elements and attributes whose names do not need to be known beforehand or specifically identified in the data mapping. This feature lets you create much more generic, flexible, and reusable mappings that require less manual intervention if data models evolve.

News about Dynamic Access to Node Names in MapForce 2017

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Faster EDI Data Translation


Electronic Data Interchange (EDI) standards accelerate commerce worldwide by allowing companies and other organizations – even those in different regions, using different languages and currencies – to send and receive unambiguous information. Some EDI communication protocols and message formats still in wide use today were developed over 30 years ago, when telecommunication systems were slower and data storage was more expensive.

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XBRL Data Mapping: WIP Taxonomy


The usefulness of the XBRL standard reaches far beyond SEC requirements for filing financial statements. Organizations such as XBRL.US and XBRL International are working to develop XBRL taxonomies and accompanying software solutions for countless other practical applications where standardizing data submission results in increased accuracy and productivity for all involved – for report filing, data analysis, and beyond.

One such project is the Work in Progress (WIP) Taxonomy created by XBRL.US for the surety industry. The new taxonomy helps save time and increase accuracy for report submission, and at the same time enables new opportunities for data analysis and decision making.

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Data Mapping REST Web Services


MapForce 2016 Release 2 includes expanded functionality for Web Services data mapping, providing robust support for REST Web services. MapForce accepts XML or JSON as the Web service response, allows definition of parameters, and supports custom HTTP headers. Users may define the Web service interface manually or by importing settings from a WADL file or a URL. Manual definition of REST Web Service Settings lets developers create settings based on a template URL. This is a convenient step when developers test and refine REST calls in a Web browser window, since the URL can be copied from the browser to become the template.

REST Web Services can be a pipeline of information for a data mapping project

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Five Not-to-Miss Features in Altova’s Latest Product Release


I’m excited to reveal some details of today’s Release 2 of Altova MissionKit 2016 desktop developer tools and software products!

v2016r2 introduces over 20 new features and updates to the Altova product line – but that’s a lot to cover in one blog post. Let’s take a look at the top five that are sure to blow your socks off.

New in Altova products

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Applying Data Mapping Patterns


Altova MapForce includes powerful mapping components that correspond to design patterns for data transformation requirements. Analyzing a data mapping challenge up front and following a few straightforward guidelines can uncover data mapping patterns that help simplify creation of the mapping design and lead to an optimal solution. The MapForce Examples project provides sample mapping files and data sets that illustrate many common data mapping patterns. Reviewing these examples and executing them with the MapForce Built-in Execution Engine is another good way to help select the best pattern for your own project.

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