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Prof. Dr. S. Decker
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
Tel +49/241/8021501
Fax +49/241/8022321

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Courses offered in WS 21/22

  • Lecture: 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 KnowledgeGraph). The module provides a theoretically grounded and practically oriented introduction to this area.

  • Lecture: 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 are discussed (e.g. query processing and transaction management). The concepts of implementation are demonstrated using classical relational DB systems as well as newer systems (distributed DB, NoSQL systems). Concepts, frameworks and components of Big Data architectures, e.g. MapReduce, Apache Spark and Apache Kafka are introduced and practically tested.

  • Lecture: Web Science

  • More than thirty years after the birth of the World Wide Web, Web Science is an established study field in Computer Science. This course introduces fundamental concepts (web centralities & basic algorithms, network models and web engineering principles) of Web Science. We will learn fundamental algorithms for web page ranking like PageRank and HITS as well as community detection algorithms. In the engineering part we dig into scalable approaches like cloud computing and peer-to-peer partly based on Post-HTTP protocols like the XMPP and WebRTC are. We will learn about Web Services and their RESTful implementation. With the knowledge gained in the preceding chapters we can analyze and engineer advanced Web applications.

  • Lecture: Privacy Enhancing Technologies for Data Science

  • This lecture covers current research results in the area of Privacy Enhancing Technologies (PETs) which can be applied to Data Science. These PETs have the potential to enable a new generation of privacy-enabled services which are not focused on maximizing the collection of user data. We use a mix of recent book chapters and papers from conferences and journals of the last few years as primary source material.

  • Proseminar: Algorithmen für die Entdeckung von Communities in sozialen Netzwerken

  • In diesem Proseminar werden sogenannte Overlapping Community Detection Algorithms (OCDA) mittels eines multi-perspektivischen Kriterienkatalogs untersucht. Neben klassischen informatischen Kriterien wie Korrektheit, Laufzeit und Speicherplatzverbrauch werden Kriterien wie Genauigkeit und Güte der gewonnen Information, aber auch die Anwendbarkeit auf bestimmte Formen sozialer Netzwerke (assoziativ und dissassoziativ) eingesetzt. Die Bewertungen werden beispielsweise durch Spinnendiagramme visualisiert. Das Proseminar bietet neben der üblichen Einführung in das wissenschaftliche Arbeiten spannende neue Formen des kollaborativen Forschen und Publizieren auf dem Web geübt. So werden die fachlichen Themen in Zweiergruppen mittels einer Wiki-Buch Plattform erarbeitet. Zusätzlich werden Gruppen zum fachlichen Begutachten, zum Web-Design und zur Animation von Algorithmen gebildet. Die Ergebnisse des Proseminars werden daher nicht in einer Schublade verschwinden, sondern auf dem Web als Open Content publiziert.

  • Seminar: Social Computing Seminar

  • Social Computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. In this seminar we explore recent topics in social computing like Social Bots, Fake News, Filter Bubbles, Socio-political campaigns, Shit & Candy Storms, Social Augmented and Virtual Reality, Gamification, Serious Games, Science 2.0

  • Seminar: 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.

  • 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.

  • Practical course (basic level): Mixed Reality Lab

  • Mixed Reality is a continuum of spatial computing experiences on virtual, augmented and extended reality devices, such as the Microsoft HoloLens, the HTC Vive, and mobile phones. In this lab, we learn the basics of mixed reality software development in hands-on lessons with practical tasks. The lab contains a small independent project student groups can propose and work on.

  • Practical course (basic level): Knowledge Graphs Praktikum

  • 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.

  • Practical course (advanced level): Data Visualisation and Analytics

  • This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.

  • Practical course (advanced level): High-tech Entrepreneurship and new Media

  • The lab course combines tutorials and guest lectures on the development of complex information products with practical experience in start-ups on specific and typical IT-related problems of the companies taking part in the lab. Integrated into the concept of this course is the development of presentation and other soft skills. Every winter term we offer projects from local and international start-ups.

Courses planned for the upcoming semester (SS 22)

  • Lecture: Social Computing

  • Social Computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. We will address social computing infrastructures, social computing engineering processes, computational social science, in particular recommender systems and community detection, crowdsourcing, collective intelligence, the dark web, mixed reality, mobile social computing, science 2.0 and advanced topics.

  • Proseminar: Algorithmen für die Entdeckung von Communities in sozialen Netzwerken

  • In diesem Proseminar werden sogenannte Overlapping Community Detection Algorithms (OCDA) mittels eines multi-perspektivischen Kriterienkatalogs untersucht. Neben klassischen informatischen Kriterien wie Korrektheit, Laufzeit und Speicherplatzverbrauch werden Kriterien wie Genauigkeit und Güte der gewonnen Information, aber auch die Anwendbarkeit auf bestimmte Formen sozialer Netzwerke (assoziativ und dissassoziativ) eingesetzt. Die Bewertungen werden beispielsweise durch Spinnendiagramme visualisiert. Das Proseminar bietet neben der üblichen Einführung in das wissenschaftliche Arbeiten spannende neue Formen des kollaborativen Forschen und Publizieren auf dem Web geübt. So werden die fachlichen Themen in Zweiergruppen mittels einer Wiki-Buch Plattform erarbeitet. Zusätzlich werden Gruppen zum fachlichen Begutachten, zum Web-Design und zur Animation von Algorithmen gebildet. Die Ergebnisse des Proseminars werden daher nicht in einer Schublade verschwinden, sondern auf dem Web als Open Content publiziert.

  • 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.

  • Seminar: 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: Web Science Seminar

  • Web Science has become an interdisciplinary study field between computer science, mathematics, sociology, economics, and other disciplines. This seminar researches advanced Web Analytics and Web Engineering topics in Web Science probably leading to master thesis topics for excellent students. Topics include: network evolution models and network dynamics, (overlapping) community detection, recommender systems, adaptation and personalization in Web Environments, the Educational Web, Web Trust & Credibility, Web Protocols, Peer-to-Peer Networking for Web Clients, Web-based Software Development Models, particular Web Development methods like Web Components and many more. Students do not only learn to write and present scientific papers but also to peer review them. Students will be assigned to a supervisor helping the student through all steps like literature research, seminar paper and seminar presentation.

  • Seminar: 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.

  • Practical course (basic level): Mixed Reality Lab

  • Mixed Reality is a continuum of spatial computing experiences on virtual, augmented and extended reality devices, such as the Microsoft HoloLens, the HTC Vive, and mobile phones. In this lab, we learn the basics of mixed reality software development in hands-on lessons with practical tasks. The lab contains a small independent project student groups can propose and work on.

  • Practical course (basic level): Basic End2End Resourcemanager

  • In this practical course, the participants learn to run a software development project and create a software product from the very beginning - from requirement analysis to release. The students will learn the importance of Scrum as part of the agile software development process.

  • Practical course (advanced level): Sovereign Data Exchange

  • In this lab, we will apply these technologies to some data exchange/data sharing scenarios. Students are expected to develop a complete workflow for a data exchange, including data preparation, policy definition, apps for enriching data, etc.

Working Groups

Specialization Areas

Themenbereiche für eine Prüfung im Vertiefungsgebiet Informatik:

  • Einführung in Datenbanken
  • Implementierung von Datenbanken
  • Informationssysteme
  • Wissensbasierte Systeme
  • Spezialvorlesungen aus den Bereichen Dokumentenmanagement, Electronic Commerce, CSCW & Groupware, deduktive Datenbanken, objektorientierte Datenbanken, Wissensrepräsentation, natürlichsprachliche Schnittstellen, Logikprogrammierung, sowie Informationssystem-Anwendungen in Büro, Ingenieurwissenschaften und Medizin