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Courses offered in SS 17

  • Lecture: Datenbanken und Informationssysteme

  • Die Vorlesung gibt einen einführenden Überblick über Datenbanken und ihre Verwendung in Informationssystemen. Wesentliche Ziele der Veranstaltung sind das Kennenlernen verschiedener bekannter Datenmodelle, wobei der Schwerpunkt auf dem relationalen Modell liegt. Es werden theoretische sowie praktische Grundlagen der Datenmodelle und zugehöriger Anfragesprachen vermittelt. Außerdem werden Modellierungs- und Entwurfstechniken für das relationale Modell vorgestellt und Einblicke in grundlegende Datenbanksystemtechniken, z.B. die Transaktionsverwaltung, gegeben.

  • Lecture: Data Driven Medicine

  • Data play an important role in medicine: Intensive care relies on monitors presenting and analysing real-time patient data, medical imaging has become a domain of massive data processing, diagnostics rely on laboratory data, and the importance of data is ever increasing: Wearable sensors, mobile communication devices and respective apps will produce data streams, which support preventive measures in healthy individuals or allow screening as a basis for data-based prevention of diseases. Last but not least: molecular biology (e.g. by gene sequencing and gene expression analysis) introduces new biomarkers, which enable new minimally-invasive diagnostics and approaches to tailoring treatments based on individual characteristics of patients (precision medicine) – which would never be possible without sophisticated processing of huge amounts of data. Medical decision making in general will be markedly influenced by data processing and data analytics. Thus, we can expect data driven medicine to gain momentum in the nearer future. This course offers a project-oriented, multidisciplinary introduction to the basics of data driven medicine. Orientation, fundamental concepts, and methodological approaches are provided by lectures. In addition, the participants will also form small interdisciplinary teams including students of computer science as well as medical students in order to plan and implement an own project, which targets prediction or decision support generated from medical data.

  • Lecture: Algorithmen und Datenstrukturen (Service)

  • Der Kurs behandelt grundlegende Algorithmen zur Sortierung, Suche und generell Arbeit mit bekannten Datenstrukturen, wie Listen, Kellerspeichern, Bäumen oder Graphen.

  • Lecture: Informationsmanagement für öffentliche Mobilitätsangebote

  • Die Vorlesung gibt eine Einführung in die organisatorischen und technischen Aufgabenstellungen bei der Planung, der Organisation, dem Betrieb und der Qualitätssicherung von öffentlichen Mobilitätsangeboten, die mit Hilfe von Ansätzen aus der Informatik und mit Informationssystemen gelöst werden können.

  • 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.

  • Lecture: Scientific Data Management

  • The lecture will give a practical introduction into the data management in scientific applications (e.g., in life sciences or engineering). In addition to the theoretical foundations, the participants will learn to use state-of-the-art technologies to manage large scale data sets.

  • 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: 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: Linked Data

  • The World Wide Web made it possible to exchange documents and services on a global level - one can access and display documents from the other side of planet instantaneous, without prior agreement. Linked Data is a name for an effort to achieve the same for data - to make data accessible, usable, queryable regardless from where the data is coming from or what the contents is. Linked Data does not replace the current Web. It adds instead an interoperable data layer based best practices, on open standards and technical components which define how data should be published and interlinked. The purpose of this seminar is to provide a conceptual and technical introduction to Linked Data and discuss individual approaches as well as state-of-the-art. An understanding of the basic concepts will then make it possible to discuss opportunities and challenges of Linked Data.

  • Seminar: Big Data in Personalized Medicine

  • Today’s health care and wellbeing technologies such as diagnostic imaging, next generation sequencing, molecular profiling, mobile and wearable technologies produce vast amount of data, which is by nature high volume, variety and velocity. Big data infrastructure and analytical methods has potential to create significant value by tailoring health care intervention and prevention to the separate needs of different groups, and yielding better outcomes. In this seminar students will explore the challenges and the state of art of applying big data technologies to the personalized medicine domain.

  • 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: Research Data Management

  • Research data are the foundation of scientific knowledge and new discoveries. Today we have a rapid explosion in scientific research. The amount of research published in 2014 (514,395) was more than triple the amount published in 1990 (136,545), more than 100 times the amount published in 1950 (4,432), and more than 3,000 times the amount published in 1940 (153). However due the lack of reproducibility, there is an increasing concern about the reliability of many experimental or observational results. With the increased digitization of research there are new possibilities to store and preserve research data with the benefits of making research more controllable and replicable. When data are available, findings described in publications can be validated, thereby increasing the trust. Also when data more easily reused, it will save resources and foster multidisciplinary research. Computer science tackle data sharing and reusability problem by developing new methods and tools to create an eScience ecosystem. In this seminar we will cover technologies to make data findable, diverse data access mechanisms, semantic interoperability technologies, data quality, provenance tracing, and reuse issues. Students with research interests in scientific data management, sharing, and reuse can participate this seminar and learn more.

  • Practical course (basic level): Scala-Programmierung und Data Science

  • Dieses Veranstaltung gibt eine Einführung in das Programmieren mit Scala. Nach einer Einführung der grundlegenden Konstrukte der Programmiersprache in mehreren kleinen Übungen, sollen die Studierende eine komplexere Projektaufgabe lösen, die den ganze Software-Entwicklungsprozess von Anforderungserfassung, Design, Implementierung, Testen, Deployment bis hin zur abschließenden Dokumentation umfasst.

  • Practical course (basic level): Blockchain Experience Lab

  • By the end of the experience lab on blockchain technology students will have an elaborated understanding of the concept of blockchains for distritbuted data and transaction management and its potential impact for the digitalization of processes and businesses.

  • Practical course (advanced level): Arbeitswelt der Zukunft: Accenture Campus Innovation Challenge

  • Die Campus Innovation Challenge ist ein von Accenture initiierter Wettbewerb für Studierende technischer und wirtschaftswissenschaftlicher Studiengänge. Die Studierenden erhalten die Möglichkeit, sich mit modernen Technologien auseinander zu setzen und von der intensiven Zusammenarbeit mit unseren IT-Beratern sowie unseren Technologie-Partnern zu profitieren. Eine große Chance, aktuelles Wissen in Projektmethodik und der Lösung praxisrelevanter Anwendungsprobleme zu erwerben.