Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Dr. Johannes Lorey

 

 

Former PhD student of the HPI Research School
at University of Potsdam
Email: Johannes Lorey

 


 

 

Research Activities

  • Cloud Computing
  • Large-Scale Data Analysis and Processing
  • Data Placement
  • Linked Data Management

Publications

Black Swan: Augmenting Statistics with Event Data

Johannes Lorey, Felix Naumann, Benedikt Forchhammer, Andrina Mascher, Peter Retzlaff, Armin ZamaniFarahani, Soeren Discher, Cindy Faehnrich, Stefan Lemme, Thorsten Papenbrock, Robert Christoph Peschel, Stephan Richter, Thomas Stening, Sven Viehmeier
In Proceedings of the 20th Conference on Information and Knowledge Management (CIKM), pages 2517-2520, Glasgow, UK, 2011

Abstract:

A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively.

BibTeX file

@inproceedings{blackswan,
author = { Johannes Lorey, Felix Naumann, Benedikt Forchhammer, Andrina Mascher, Peter Retzlaff, Armin ZamaniFarahani, Soeren Discher, Cindy Faehnrich, Stefan Lemme, Thorsten Papenbrock, Robert Christoph Peschel, Stephan Richter, Thomas Stening, Sven Viehmeier },
title = { Black Swan: Augmenting Statistics with Event Data },
year = { 2011 },
pages = { 2517-2520 },
month = { 0 },
abstract = { A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively. },
address = { Glasgow, UK },
booktitle = { Proceedings of the 20th Conference on Information and Knowledge Management (CIKM) },
priority = { 0 }
}

Copyright Notice

last change: Fri, 17 Apr 2015 11:38:34 +0200

Teaching Activities

Co-supervised Master's thesis

  • Armin Zamani-FarahaniStrategies for structure-based rewriting of SPARQL queries for data prefetching, 2013