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Informatik Kolloquium: Aktuelle Themen der Informatik

Prof. Dr. Steffen Staab

Semantics Reloaded

Steffen Staab, Uni Koblenz

In phase 1 of semantic technologies, research focused on explicitly modeling the semantics of data and knowledge in ontologies and likewise models. In phase 2 of semantic technologies, people tried to learn semantics of data from how it was used with other data by means of terminology inducation and ontology learning using machine classification and clustering. While both of these approaches remain valid undertakings, we posit that one should now also consider a phase 3 of semantic technologies. In this phase, semantics arises from what we do with data and knowledge.

We give some, very heterogeneous, examples from our current projects:

  1. Studying users in social networks with transfer learning
  2. Studying the semantics of user interactions with eye tracking
  3. Studying the usage of data in programs with logical inference.

10 November 2017, 14:00
Multimedia Room, 15th Floor, L3S

Prof. Dr. Bettina Berendt

Why formalizing fairness won't fix algorithmic discrimination

Bettina Berendt, KU Leuven

‘‘Big Data’’ and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, I describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. I describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. I derive data protection by design strategies for making decision- making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors.

24 November 2017, 14:00
Multimedia Room, 15th Floor, L3S

Dr. Bastian Pfleging

Automotive User Interfaces in the 21st Century: Challenges to Support Drivers in the Transition from Manual to Automated Driving

Bastian Pfleging, Ludwig-Maximilians-Universität München

Our daily life becomes more and more digital through the integration of digital, connected, and ubiquitous systems. Even the car is much more than a vehicle to get from A to B: Besides conducting the primary task of driving the car, drivers want to perform a multitude of non-driving-related activities (e.g., operate the infotainment system, communicate with the outside world etc.) at the same time. The imminent risk is that such activities distract the driver and, thus, put drivers, passengers, and the environment at risk. Therefore, one important challenge is the exploration and design of automotive user interfaces which enable safe, diverse, exciting, and easy-to use means to perform also additional activities in the car.
With the transition to automated driving, non-driving-related activities become even more important and will be one major selling point and field of innovation for future cars. One important aspect will be the driver-vehicle interface which provides enough freedom to perform a multitude of activities (e.g., office work, communication, relaxation, media consumption) but still ensures a safe fallback to manual driving. In my talk, I will present our research activities in this domain of automotive user interfaces and provide insights into our current projects. This includes interaction aspects, driver monitoring, concepts for non-driving-related activities, and aspects of automated driving.

8 December 2017, 17:00
Multimedia Room, 15th Floor, L3S

Prof. Dr. Volker Markl

Mosaics in Big Data: Stratosphere, Flink and beyond

Volker Markl, TU Berlin

The global database research community has greatly impacted the functionality and performance of data storage and processing systems along the dimensions that define “big data”, i.e., volume, velocity, variety, and veracity. Locally, over the past five years, we have also been working on varying fronts. Among our contributions are: (1) establishing a vision for a database-inspired big data analytics system, which unifies the best of database and distributed systems technologies, and augments it with concepts drawn from compilers (e.g., iterations) and data stream processing, as well as (2) forming a community of researchers and institutions to create the Stratosphere platform to realize our vision. One major result from these activities was Apache Flink, an open-source big data analytics platform and its thriving global community of developers and production users. Although much progress has been made, when looking at the overall big data stack, a major challenge for database research community still remains. That is, how to maintain the ease-of-use despite the increasing heterogeneity and complexity of data analytics, involving specialized engines for various aspects of an end-to-end data analytics pipeline, including, among others, graph-based, linear algebra-based, and relational-based algorithms, and the underlying, increasingly heterogeneous hardware and computing infrastructure. At TU Berlin, DFKI, and the Berlin Big Data Center (BBDC), we aim to advance research in this field via the Mosaics project. Our goal is to remedy some of the heterogeneity challenges that hamper developer productivity and limit the use of data science technologies to just the privileged few, who are coveted experts.

19 January 2018, 14:00
Multimedia Room, 15th Floor, L3S