5 Reasons to Choose a Graphical JSON Schema Editor


The advantages of JSON as a lightweight, human-readable, interoperable data format have led to its widespread adoption in various domains, including web development, mobile app development, and backend services. Many programming libraries and frameworks provide built-in support for JSON parsing and serialization.

That said, most applications still benefit from or require validation of client-submitted data. Enter the JSON Schema spec, which lets you describe the structure of JSON data for a particular application, for both documentation and validation purposes.

Though JSON Schema code is by design human-readable, building a complex schema with nested and repeating sections in a text-only editor becomes time consuming and error-prone quickly. Let’s look at five ways a graphical editor is a must-have for JSON Schema development.

JSON Schema Editor in XMLSpy
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AI-based Database Image Classification with Altova MapForce


One of the most common examples of AI in our everyday lives is facial recognition. Facial recognition is the process of identifying or verifying a person’s identity based on their face. Facial recognition is used in many applications, such as unlocking our phones with FaceID, tagging our friends on social media platforms like Facebook, and checking in at airports or hotels with biometric scanners. Facial recognition can make our lives more convenient and secure, but it can also raise some privacy and ethical concerns. For instance, how can we ensure that our facial data is not misused or stolen by hackers or malicious actors? How can we prevent facial recognition from being used for surveillance or discrimination? How can we ensure that facial recognition is accurate and fair, and does not have any biases or errors?

The paragraph above was generated by ChatGPT in response to my request to describe the benefits and risks of artificial intelligence and include a real-life example. It’s interesting that ChatGPT chose FaceID as the example, since FaceID is simply one variation of image analysis and AI-powered image classification offers potential to automate many real-world tasks.

One common use-case is a product catalog, wherein a company manages product information provided by many different manufacturers. A product loaded into that database may have a name that does not necessarily include a precise description of the item. For instance, wellington is a boot, fedora is a hat, a mongoose is a bicycle, and a yellow watermelon shiny needlefish is a fishing lure. We can make use of AI-powered image classification using the Microsoft Azure Cognitive Services Computer Vision API to address this problem. The Computer Vision Service takes the image data or URL as its input and returns information about the content. One service generates image classification tags based on a training set of recognizable objects, living beings, scenery, and actions that the Azure AI has been trained on. These tags allow us to categorize products in the database accordingly and may even correspond to search terms a user might provide to find products in the catalog.

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AI-based support request sentiment analysis using MapForce and GPT-4


Automated sentiment analysis of text, such as user reviews, has historically been a challenge. Because of the myriad intricacies of natural language, systems faced difficulties in analyzing context and nuances. This required an inordinate amount of manual work to overcome.

One of the many useful capabilities of modern AI systems that are based on large language models (LLMs) such as OpenAI’s GPT-4 is that they are very good at sentiment analysis of natural text inputs. We can use that capability to build a very efficient database solution in MapForce that, for example, goes through all the new incoming records in a support database and automatically determines whether a particular support request or other customer feedback is positive, negative, constitutes a bug report, or should be considered as a feature request.

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How to Create a Chained Data Transformation


Data mapping plays a vital role in modern data-driven organizations, enabling efficient data management and integration. Altova MapForce is a powerful, graphical data mapping tool that supports endless data transformation scenarios, including one-to-one, one-to-many, many-to-one, and chained data conversion.

While there are applications for each of these approaches, chained data mapping is especially helpful for complex data processing tasks where multiple stages of data manipulation are required. Here’s a look at the benefits of a chained data conversion approach – and a video of how MapForce makes the process easy and straightforward.

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How to Convert Data in MapForce [Video]


Altova MapForce offers a powerful, yet easy-to-use, approach to converting and transforming data. Whether you need to write XML to a database, convert JSON to EDI, or map Excel to multiple different data formats, MapForce has you covered.

From simple one-to-one conversions to complex ETL scenarios, the MapForce approach is to represent data structures as graphical components. To associate fields, drag and drop connecting lines. A comprehensive library of data filters and functions is available for transforming data before writing it to the target.

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We have recently revamped our series of Introduction to MapForce videos. Each short how-to gives a demo of a common MapForce scenario.  

Start at the beginning to learn how MapForce works:

And follow along to learn:

You can follow along with the examples in these how-to videos by downloading a free, 30-day trial of MapForce. Check back for new MapForce videos, which are added often.

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New Tools for Large XML and JSON Documents


Code editors with a graphical option in addition to the traditional text view are great for developers working with XML or JSON. Graphical editors like Grid View in XMLSpy provide a visual representation of the JSON or XML structure, making it easier to understand, navigate, and edit complex data hierarchies.

XMLSpy introduced Grid View years ago and has been improving on it ever since. The most recent update includes the ability to split the editing pane horizontally or vertically, which is especially handy when working with large documents.

Let’s see how it works.

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Creating ESEF Filings Just Got Easier


To meet current mandates, ESEF (European Single Electronic Format) reports must be filed in XBRL. To accelerate the process, regulated companies can take advantage of intelligent software tools to fill the reports with data. One such tool from Altova allows organizations to create valid ESEF XBRL directly in Excel, where finance pros are already comfortable working. This allows them to create reports for filing without having to learn the intricacies of XBRL syntax.

Unlike some other XBRL filing mandates, ESEF requires filing companies to create their own extension taxonomy, which defines the entity-specific data rules for their report. To help customers with this step of the filing process, the Altova ESEF XBRL add-in for Excel includes a visual extension taxonomy designer that makes it a complete start-to-finish ESEF reporting solution.

Let’s see how it works.

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