big-data

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.

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in …

National economies rest on networks of millions of customer-supplier relations. Some companies – in the case of their default …

Mobility monitoring in Austria during the COVID NPI

Execute the latest version of spark on HDP.

Use additional python packages with pyspark

Make spark jobs scale reliably using iteration

Use idempotency of RDD’s to your advantage

Bring hexagons as efficient spatial operations to spark

recent history of data processing.