The IfI Research Talks will be held at the date indicated below. They usually start at 13:30 (check exact time in the table below) and last for about 30 minutes.

Date	Speaker	Title	Venue
Wednesday 21.12.2022 13:45	Prof. Dr. Jürgen Bernard Interactive Visual Data Analysis Group	Bi-Directional Human-Machine Feedback to Leverage Human Data Preferences	BIN 2.A.01
Wednesday 08.03.2023 13:30	Prof. Dr. Dan Olteanu Data Systems and Theory Group	Real-time analytics over continuously evolving databases	BIN 2.A.01 and online*
Wednesday 19.04.2023 13:30	Prof. Dr. Alberto Bacchelli Zurich Empirical Software Engineering Team	Title TBA	BIN 2.A.01 and perhaps online*
Wednesday 31.05.2023 13:30	Prof. Dr. Manuel Günther Artificial Intelligence and Machine Learning Group	Title TBA	BIN 2.A.01 and perhaps online*
Wednesday 08.11.2023 13:30	Prof. Dr. Martin Volk Text Technologies	Multilingual Text Analysis, ChatGPT and Digital Humanities	BIN 2.A.01 and perhaps online*

* IfI-internally

Speaker: Prof. Dr. Jürgen Bernard

In his Talk, Prof. Jürgen Bernard motivates the benefits of human-machine collaboration to solve data science and analysis challenges. Along these lines, he outlies typical bottlenecks in the human-machine collaboration, and gives indications on how his research helps solving these problems.

In particular, Jürgen Bernard emhpasizes the potentials of leveraging preferences of humans when it comes to data analysis, and discusses how interactive machine learning systems can replay the information that was learned from humans expressing feedback.

In two real-world examples, Jürgen Bernard shows how bi-directional human-machine feedback can be applied in practice, to leverage data preferences: for the preference-based creation of item rankings and human-centered similarity search.

Links: IVDA Group  |  Digital Society Initiative

Speaker: Prof. Dr. Dan Olteanu

Abstract:

In this talk I will overview recent efforts of the DaST (Data Systems and Theory) group on the problem of maintaining real-time analytics over large and continuously evolving relational databases.

We looked at the maintenance problem from different angles. We addressed several fundamental questions, including:

• Which database queries can be maintained in worst-case optimal time?
• Can we trade the query answer time for fast update time?
• How can we maintain models trained over changing relational data?

Our answers include:

• An algorithm with the best possible (worst-case) computational complexity for maintaining the number of triangles in a graph under insertions and deletions of edges.
• The computational complexity trade-off between update time and the answer time for a large class of queries.
• A new algebraic structure called the covariance ring that defines the shared maintenance of the covariance matrix for features in the database.

Equipped with a theoretical understanding of the maintenance problem, we focused on the design and implementation of maintenance systems:

• F-IVM (https://github.com/fdbresearch/FIVM) is our open-source state-of-the-art system. Using one thread of a commodity machine, F-IVM can maintain queries and linear regression models over database joins with a throughput of ten million updates per second.
• The RelationalAI commercial engine (https://relational.ai) incorporated some of our theoretical and system design ideas.

This is joint work with DaST members Ahmet Kara and Haozhe Zhang, Milos Nikolic of U. Edinburgh, and Henrik Barthels, Mohamed ElSeidy and Niko Göbels from the RelationalAI IVM team.