Released: Oct 21, 2025

2026 での更新項目

機能

• Support for OCR processing of PDF files
  • MapForce includes the powerful PDF Extractor that lets you visually define the structure of a PDF document to efficiently extract its data for use in a data mapping project. The PDF Extractor is a highly flexible tool that allows you to extract only portions of text instead of the whole document, mix and match pieces of information from different pages of the same PDF file, split tables into rows, and arrange data into groups.
  • While this functionality is immediately useful for digital, text-based PDFs, many PDFs are actually scanned documents, i.e., just images. New support for OCR (optical character recognition) lets MapForce turn those images into selectable, searchable text available for extraction. This allows the MapForce PDF Extractor to process a broader range of inputs, including older documents, digitized paper archives, and scanned or handwritten forms.
  • When you run OCR on a scanned PDF in MapForce, the PDF Extractor displays the detected content in a tree of objects. An overlay of the document itself shows how the OCR processor has detected words in the scan area, displaying recognized words in green. Words highlighted in red were not added to the tree, as their confidence score did not meet the processor's threshold. You can edit the tree as well as green and red words manually, as required.
  • When only a portion of the data is required, or when working with large documents, you can define a ScanArea with your mouse to run OCR on one region of the document at a time.
  • When OCR and editing are complete, you can save the results and continue creating your PDF data extraction rules in the PDF Extractor.
• Support for decision tables
  • A decision table is a structured way to represent business rules or logic by laying out all possible conditions and the corresponding if/then/else actions in a tabular format. For instance, a decision table for loan approvals might list conditions like credit score, income level, and employment status, and then show the corresponding actions such as approve loan, approve with conditions, or deny.
  • A decision table consists of conditions (inputs) and actions (outputs). Each row in a decision table represents a rule.
  • Such a set of conditions can be helpful in a data mapping project to define how to process data when multiple criteria need to be evaluated. Rather than requiring users to configure each possible condition and action separately, the new decision table component makes it easy to define numerous criteria for processing rules at once, and then compress them into an easy-to-understand MapForce component that processes input data according to the defined rules. The decision table shown above is used by the mapping below to write the appropriate discount amount to the target data structure.
  • This approach makes complex mapping logic clear, consistent, and easy to maintain, especially when multiple conditions determine the target values.
• Guide bar for new users
  • The visual, drag-and-drop interface in MapForce makes mapping straightforward, but, as with any tool, new users may find it tricky to know where to begin when starting with a blank project. The new MapForce guide bar helps walk you through creating a mapping step-by-step.
  • You can start by viewing a brief how-to video to familiarize yourself with different aspects of mapping or simply click Start to move to the first step of defining a new project.
  • The guide bar steers users through inserting source and target data mapping components by choosing among the supported formats listed in an easy to understand, visual Component Gallery.
  • Then, the guide explains how to define the mapping logic by drawing connection lines and inserting additional processing components like filters or functions. Processing components are available in the Libraries pane, where they can be dragged into the mapping area. Users can also insert some of the most common data processing components from the visual Component Gallery, where they are listed with helpful descriptions.
  • Finally, the guide tells you how to execute the mapping to validate and save the output. By clicking through the guide, you can define a working data mapping project quickly while also learning about the extensive functionality available in the software. It's a great way to get started in MapForce.
• Dialog for adding new components
  • The Component Gallery in the guide bar for inserting data mapping components and processing functions described above is also available via the Insert menu, giving new and experienced users a quick and easy way to set up their projects, with larger icons and informative descriptions for each option.
• Updated EDI support
  • MapForce can map variety of EDI formats with support added in the latest version for SWIFT 2025, EDIFACT 2024A, and EANCOM 2002, which is a subset of the EDIFACT standard.
• Java Classpath support for deploying mappings to MapForce Server and FlowForce Server
  • Simplified classpath handling makes it easier to include and execute custom Java classes in data mapping projects deployed to MapForce and FlowForce Servers.
• Updated relational database support
  • In this release, support has been added for new versions of:
    • Firebird 5.0.
    • MariaDB 11.4 and 11.8.
    • PostgreSQL 17.
• Support for MongoDB 8.0
  • For NoSQL data mapping, MapForce 2026 includes support for the latest version of the MongoDB database.
• Support for Visual Studio Insiders 2026
  • In addition to support for integration in previous versions of Visual Studio, MapForce 2026 supports this pre-release version of Visual Studio from Microsoft.
• Support for Eclipse 4.34, 4.35, 4.36, 4.37
  • MapForce supports seamless integration in the Eclipse IDE with new support added for the latest versions.
• Support for Windows Server 2025
  • New support has been added for running MapForce on Windows Server 2025.