5 papers
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía +1
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can sp…
Revisiting Non-Specific Syndromic Surveillance
Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
Infectious disease surveillance is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as ear…
DeepDB: Learn from Data, not from Queries!
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa +3
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. T…
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the d…
Model-based Approximate Query Processing
Moritz Kulessa, Alejandro Molina, Carsten Binnig +2
Interactive visualizations are arguably the most important tool to explore, understand and convey facts about data. In the past years, the database community has been working on di…