Efficient Temporal Graph Analytics
Oct 19, 2022
AI basierte Root Cause Analyse von CPD Störquellen in Docsis Netzen
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream \emph{high noise} in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.
May 10, 2022
Orchestrating data in the mesh of the fragmented modern data stack
The fragmented modern data stack has emerged as the unbundling of Airflow. Various tools operate in silos. Dagster as a next-generation data orchestrator allows you to clearly see the data dependencies of the individual pipelines on your data factory floor.
Apr 27, 2022
Mobility analytics
Nov 16, 2020
data processing
recent history of data processing.
Aug 4, 2019
Geospatial data processing
https://docs.google.com/presentation/d/1wBMFVv-4iaVfbmH5GNynvSaTAMQtWpUoHuMNHP83Uys/edit?usp=sharing
May 27, 2018
R for HPC and big data
https://docs.google.com/presentation/d/1VgocCul4ZyLVDqqr6iXsBRmmwsGhJkZj6-lzgdXs0ag/edit?usp=sharing A similar talk was held at a Meetup in December 2018 in Vienna using the following slides and focusing on sparklyR: https://docs.google.com/presentation/d/1NHG7-WoEUsjrdxFjy01OmZjxWB-FZomhfrxO-QapzKg/edit?usp=sharing There is even a videom from the talk. It was one of my first video recorded talks ;)
Sep 27, 2017
Make your ML app rock
Dec 10, 2016