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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Building Apps with an Intelligent Database Wizard


Data-driven solutions like database and enterprise apps rely on connection to, and interaction with, backend databases. Backend relational databases, however, store data in tables that reflect complex data relationships. This provides numerous advantages for effective data management and data integrity but can make it difficult to access and work with the data stored therein in new ways. App developers need to have a comprehensive understanding of database design principles and the SQL query language just to get started.

In contrast, real world data relationships most often represent parent-child relationships or even deeper hierarchical structure. As such, working with hierarchical data where relationships can be visualized in a tree structure can be much simpler and more flexible, leading to faster development. This approach is also more accessible to developers without extensive SQL expertise.

To make building apps that connect to the backend relational databases that are ubiquitous in today’s enterprise easier, faster, and available to a wider range of developers, Altova MobileTogether takes an entirely unique approach. Its visual Database Wizard lets developers easily build a query that returns hierarchical data, work with that data in the app, and then easily save the data back in hierarchical form, letting MobileTogether take care of normalizing the data and writing it back to the corresponding linked tables. Let’s take a look at how it works.

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Role-based Access Control in Enterprise Apps


Enterprise database apps are increasing in prevalence due to their advantages for enabling access to—and easy management of—the ever-growing amount of critical data business users need to work with on a day-to-day basis. Unlike other types of business productivity apps, database apps must include measures for managing different levels of user access to maintain the security and integrity of the enterprise data they expose.

This can include managing read-only and editing access rights or restrictions on access to certain types of data. While it is essential to ensure that only authorized personnel have access to confidential data, levels of permissions often vary throughout an organization. Apps built using Altova RecordsManager include comprehensive tools for managing role-based access to database data that can reflect these complicated relationships that exist within an organization.

Let’s take a look at how RecordsManager makes it easy for app administrators to manage complex role-based permissions with visual tools.

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Getting Started with Altova RecordsManager


RecordsManager is a new tool from Altova to build business database solutions in record time using a powerful visual design interface. RecordsManager is a free, pre-built MobileTogether solution that is automatically available when you install MobileTogether Designer. The pre-built solution includes sample data sets, and the MobileTogether Simulator previews execution of the database solution right inside the free to use MobileTogether Designer. Getting started with Altova RecordsManager is just one click away when you launch the Designer. Soon you will be building your own custom database apps without needing backend development or manual coding.

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Build No-Code Database Apps with RecordsManager


We are excited to announce availability of a new product in the Altova app development framework: RecordsManager.

Altova RecordsManager offers a completely visual, no-code interface for quickly creating custom database apps. RecordsManager is perfect for any app that handles data in records: think contract management, a customer database, an invoicing system, a database of local attractions or collections – the sky is the limit.

Your RecordsManager app will automatically be available on desktop devices as well as on mobile using native iOS and Android apps and provides tons of features that make it easy for end-users. Let’s see how it works.

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How to Compare CSV Files or Compare a CSV File to a Database Table


CSV files are a quick and convenient way to record structured data in a generic format. Because CSV files are so easy to create, multiple similar versions of very large CSV files can quickly proliferate. Often it becomes necessary to compare CSV files to find the desired version. In an ETL scenario, a data analyst may want to compare a CSV file to a database table for validation or to update data.

DiffDog, the unique XML-aware diff / merge tool from Altova, supports CSV as a native file format for comparison and can compare and selectively merge data CSV to CSV, or between a CSV file and database table. Let’s look at an example.

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