Kategorie: ‘Courses’
Software Projektpraktikum – Building Large Language Model Applications
Social Computing Seminar
Social Computing embodies the intricate interplay between evolving computational systems and dynamic societal behaviors. This field examines how technology can be crafted to interpret and enhance human interactions and observes these systems’ transformative influence on our social fabric.
Implementation of Databases
The lecture gives an introduction to the implementation of database systems. Besides the rough architecture of a DB system, detailed methods for solving individual DB tasks, such as query processing and transaction management, are presented. The concepts of implementation are demonstrated using classical relational DB systems as well as distributed and NoSQL systems. Concepts, frameworks and components of Big Data architectures, e.g. MapReduce, Apache Spark and are introduced and practically tested.
Semantic Web
As part of the W3C Semantic Web initiative standards and technologies have been developed for machine-readable exchange of data, information and knowledge on the Web. These standards and technologies are increasingly being used in applications and have already led to a number of exciting projects (e.g. DBpedia, semantic wiki or commercial applications such as schema.org, OpenCalais, or Google’s Freebase).
Knowledge Graphs Seminar
Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.
Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.
The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).
Deliverables of this seminar
This seminar consists of an introductory course on Knowledge Graphs. You will give a short outline presentation on your assigned topic to set overview and expectations about the paper you’re going to write. The main deliverable of the seminar is a paper that describes the state of the art of your assigned topic. While you do not need to contribute original research, your task is to show the scientific competences of literature research, presentation of a research question and understanding and putting relevant papers into context. Furthermore, you are asked to critically assess and compare strengths or challenges of existing solutions. You will review your peer’s papers and give relevant feedback to enhance your scientific writing skills. You will present your paper in a final presentation in a block seminar at the end of the semester.
Seminar Data Ecosystems
Organizations in many domains, such as manufacturing or healthcare, have a huge demand to exchange data to enable new services, drive research and innovation, or improve patient care.
Hence, organizations require alliance-driven infrastructures capable of supporting controlled data exchange across diverse stakeholders and transparent data management. Data Ecosystems are distributed, open, and adaptive information systems with the characteristics of being self-organizing, scalable, and sustainable trying to fulfil these requirements.
But there are many open issues, which make the exchange on a technological, processual, and organizational level a challenge. In this seminar, we will identify and discuss the main challenges in data ecosystems, such as data quality, data transparency, and data integration.
Seminar Artificial Intelligence in Circular Economy Applications
This seminar offers a collaborative and research-focused exploration of how Artificial Intelligence (AI) can be effectively employed to address challenges within the circular economy. Students will actively contribute by conducting their own research and presenting their findings.
Seminar Large Language Models – UCD-driven Metrics and Benchmarks
The development of a user-centered quality metric for the outputs of large language models in corporate contexts addresses a key challenge: How can the quality and relevance of AI-powered systems be effectively evaluated and enhanced to optimally meet the specific requirements of companies and their employees? The motivation for this research concept stems from the necessity to develop a systematic and quantifiable method for assessing user satisfaction and the usefulness of LLM outputs.
Mixed Reality Lab
Mixed Reality is a continuum of spatial computing experiences on virtual, augmented and extended reality devices, such as the Microsoft HoloLens 2, the HTC Vive Pro, Meta Quest 2 and Android smartphones. In this lab, we learn the basics of mixed reality software development in independent project work that student groups can propose and elaborate.
Seminar Privacy and Big Data
This seminar is about new and emerging approaches to adjust and balance privacy and utility in data intensive applications, such as information retrieval, data mining and personalisation. These new approaches have the potential to enable a new generation of privacy-enabled services which are not focused on maximizing the collection of user data. Instead these new approaches enable user privacy under different threat models, such as protecting the identity of individual users when querying aggregated data, or preventing leakage of query patterns when users retrieve data from a database. As a result, these new approaches may help businesses in their compliance with increasingly regulatory trust and reinforce user trust, while enabling new business models at the same time.