Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 203-207

Data-Driven Exploration of Real-Time Geospatial Text Streams

Conference paper

DOI: 10.1007/978-3-319-23461-8_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)
Cite this paper as:
Bosch H., Krüger R., Thom D. (2015) Data-Driven Exploration of Real-Time Geospatial Text Streams. In: Bifet A. et al. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9286. Springer, Cham

Abstract

Geolocated social media data streams are challenging data sources due to volume, velocity, variety, and unorthodox vocabulary. However, they also are an unrivaled source of eye-witness accounts to establish remote situational awareness. In this paper we summarize some of our approaches to separate relevant information from irrelevant chatter using unsupervised and supervised methods alike. This allows the structuring of requested information as well as the incorporation of unexpected events into a common overview of the situation. A special focus is put on the interplay of algorithms, visualization, and interaction.

Keywords

Stream processing Machine learning Social media 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Visualization and Interactive SystemsUniversity of StuttgartStuttgartGermany

Personalised recommendations