Data interoperability is becoming a more and more important part of software engineering. Software systems are often built around data and processing and exchanging data with other systems. For this exchange to work, a governing data specification is needed. Such a data specification typically consists of, e.g., data schemas, human-readable documentation, examples, etc. However, at the moment, these specifications often have the form of a set of individual, disconnected artifacts. Within this project, we work on a machine-readable representation of a data specification and its contents, including relations to other specifications.The main contribution of this project is the development of a graphical editor for the conceptual model part of the specification otherwise managed in Dataspecer.

You've probably heard of APIs and how they enable different software systems to communicate with each other. In today's world, having a well-defined and documented API is crucial for software and data development. However, creating APIs is not a walk in the park. You need to define the operations and input and output data structures, which can be challenging when dealing with complex software systems. Moreover, maintaining a set of consistent and semantically correct APIs across different versions is no easy feat.

That's where the Dataspecer tool comes in. It currently supports automated creation of data schemas, but the goal of this project is to extend its capabilities to define entire APIs in industry-standard formats such as OpenAPI and AsyncAPI. By using a defined conceptual data model, we aim to generate not just input and output data structures, but also API operations and understandable documentation. We aim at simplifying the task of defining and maintaining APIs for complex software systems.

By participating in this project, you'll gain valuable experience in cutting-edge software development practices and work with industry-standard API specifications like OpenAPI and AsyncAPI. You'll contribute to enhancing the Dataspecer tool, revolutionizing API creation and maintenance across various systems. Don't miss out on this chance to be part of an innovative project that could redefine the future of API development.

Conceptual modeling plays a crucial role in data and software engineering, serving as the foundation for designing complex systems and offering a clear representation of the domain. However, creating precise and comprehensive conceptual models remains a challenge for many engineers. In this project, we aim to explore how large language models can be harnessed to develop an intelligent assistant that guides human modelers in creating structured conceptual models from an abundance of existing resources, including unstructured verbose textual sources, emails, and stakeholder interview notes, as well as structured resources, including existing database structures or excel spreadsheets where users collect their proprietary data. The goal is to support and streamline the modeling process, enhancing the overall quality of data and software engineering projects.

In this project, students will have the opportunity to delve into cutting-edge artificial intelligence and data engineering technologies by working with large language models and applying them to real-world problems. The project's success could open new doors for AI-driven conceptual modeling assistance tools and revolutionize how we design and develop intricate systems. By joining this project, you'll contribute to the future of data and software engineering, honing your skills and making a tangible impact on the field. Don't miss this chance to be part of an innovative research endeavor that could transform the way we approach conceptual modeling in data and software engineering with the help of intelligent assistants.

Imagine you're working at your university's data center, where there are countless databases containing tons of valuable information. These databases aren't just used for the school's own systems, but they're also used to generate reports, analyze data, and manage information in general. Basically, the entire school depends on being able to find answers to questions hidden in the data as quickly as possible. But there's a problem - there are only a few employees who really understand both the organization and the databases, and they're the only ones who can translate management's questions into database queries. This creates a bottleneck, where the people who have questions far outnumber the employees who can help them.

The goal of this project is to create a chatbot that anyone in the organization can use to ask questions. The chatbot will refine the query through conversation and eventually translate it into an evaluatable database query. This means that instead of relying on a few knowledgeable employees, anyone can get the information they need quickly and easily. To make this possible, we'll use Large Language Models (LLMs). But we won't stop there - we'll refine the chatbot's knowledge with a conceptual data model of the organization and technical data artifacts generated from it in Dataspecer.

By joining this research project, you'll play an important role in transforming how your university's data center operates, making valuable information more accessible to everyone. You'll have the opportunity to work with cutting-edge Large Language Models and contribute to the development of a chatbot that streamlines database querying. This experience will not only enhance your technical skills but also enable you to make a tangible impact on the way data-driven insights are obtained within the organization. Don't miss this chance to be part of an important project that combines advanced technology and practical problem-solving.