Reliable Spatio-temporal Signal Extraction and Exploration from Human Activity Records

  • Christian Sengstock
  • Michael Gertz
  • Hamed Abdelhaq
  • Florian Flatow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)


Shared multimedia, microblogs, search engine queries, user comments, and location check-ins, among others, generate an enormous stream of human activity records. Such records consist of information in the form of text, images, or videos, and can often be traced in time and space using associated time/location information. Over the past years such spatio-temporal activity streams have been heavily studied with the aim to extract and explore spatio-temporal phenomena, like events, place descriptions, and geographical topics. Despite the clear intuition and often simple techniques to extract such knowledge, the amount of noise, sparsity, and heterogeneity in the data makes such tasks non-trivial and erroneous. This demonstration offers a visual interface to compare, combine, and evaluate spatio-temporal signal extraction and exploration approaches from large-scale sets of human activity records.


Signal Extraction Place Semantic Sparse Feature Weak Label Context Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Sengstock
    • 1
  • Michael Gertz
    • 1
  • Hamed Abdelhaq
    • 1
  • Florian Flatow
    • 1
  1. 1.Database Systems Research GroupHeidelberg UniversityGermany

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