Kategorie: ‘Courses’
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 Data Stream Management and Analysis
Low-cost sensors and high communication bandwidths open up new possibilities for applications that benefit from a high amount of data. Such applications produce data continuously, potentially unbounded, and at high rates, which is subsumed under the term data stream. Examples for applications fields are smart manufacturing, high-speed trading, fraud detection, robotics, or social networks. Data stream management systems are special systems which address the specific requirements handling data streams. In this seminar we will research recent topics in data stream management and analysis, such as data compression, online learning, or operator distribution. The seminar will be offered as block seminar.
Knowledge Graph Lab SS 2024
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.
Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.
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”).
In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:
- Graph representation of data
- Knowledge Graph basics
- Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
- Use of vocabularies and ontologies as schemas for graphs
- Searching information in knowledge graphs
- Information extraction into knowledge graphs
- Data mining techniques for knowledge graphs
- Knowledge graph completion (predicting links, finding anomalies)
- Data governance aspects, e.g., data quality
- Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)
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.
Data Science in Medicine
Health data analytics is one of the main drivers for the future of medicine. Various sources of big data, including patient records, diagnostic images, genomic data, wearable sensors, are being generated in our everyday life by health care practitioners, researchers, and patients themselves. Data science aims to identify patterns, discovering the underlying cause of diseases and well being by analyzing this data.
Web Science Seminar
Web Science is an advanced interdisciplinary field that intersects computer science, mathematics, sociology, economics, and other fields.
This seminar explores aspects of the World Wide Web, focusing on cutting-edge research in Web Analytics, Web Engineering, and aspects of Web Science that may lead to thesis topics for students.
The seminar covers a variety of areas such as algorithms and research for social aspects of the web such as recommender systems, social network analysis, and data mining as well as chatbots. A large part then is the educational web with mixed reality and psychomotor learning and the detection of academic trends. Lastly, software development trends are covered for web trust & credibility, web APIs & protocols, web-based software development models, and data security.
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).
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.
Bridge Course Databases
A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.
Bridge Course Databases
A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.