DBIS

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.

A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.

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.

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.

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.

A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.

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.