Courses offered in WS 16/17
Lecture: Web Science
Lecture: Implementation of Databases
Lecture: Big Data in Medical Informatics
Lecture: Semantic Web
Seminar: Privacy and Big Data
Seminar: Linked Data
Seminar: Big Data in Personalized Medicine
Seminar: Big Data Integration and Quality
Practical course (basic level): Unternehmensgründung und neue Medien
Practical course (basic level): Data Visualisation and Analysis Lab.
Practical course (advanced level): High-Tech Entrepreneurship and New Media
More than twenty years after the birth of the World Wide Web, Web Science has been becoming a new 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 chapter we can analyze and engineer advanced Web applications like recommender systems, collaborative editing and Web multimedia environments.
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.
The lecture introduces basic technologies of the realization of database systems. Beside the coarse architecture (e.g. layered architecture) detailed procedures for the solution of special database tasks (especially query analysis and transaction management) will be discussed. The concepts of implementation will be applied to classical (relational model) as well as to more recent data models (distributed, XML). In addition to necessary theoretical fundamentals, practical concepts will be introduced that allow database administrators the efficient tuning of databases.
This module will cover methods for the representation, interpretation and analysis of biomedical data. We will cover data interoperability methods, standards, terminologies for electronic health records and genomic data, as well as predictive analytics and decision making systems in medicine. Algorithmic and methodological approaches will be introduced with practical applications using the R programming language and the Galaxy open source platform. The topics of the lectures are • Biomedical data sources and standards • Biomedical data interoperability • Semantic technologies in biomedicine • Methods and tools for biomedical analysis • Predictive analytics and decision making systems • Genomic data analysis
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.
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.
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.
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.
The Big Data era is upon us: data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. This seminar will discuss recent results in Big Data research, e.g., Big Data integration, Data Quality, Big Data Analytics, Text Mining, and Data Stream Processing.
Unternehmensgründung und neue Medien ist ein Praktikum für Bachelor-Studenten, in dem die Erstellung eines komplexen Informationsprodukts erlernt wird. In diesem Semester stehen RESTful APIs in einer peer-to-peer Umgebung im Mittelpunkt. Voraussetzungen sind dabei Kenntnisse aus den Vorlesungen Programmierung sowie Datenstrukturen und Algorithmen.
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.
The course combines tutorials and 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. This term we offer projects ranging from mobile cloud computing to NoSQL databases.