@inproceedings{PB-VRVis-2017-030,
author = {Splechtna, Rainer and Podaras, Silvana and Beham, Michael and Gra{\v{c}}anin, Denis and Matkovi{\'c}, Kresimir},
title = {MC2 — Spatio-Temporal Provenance Data Aggregation for Visual Analysis},
year = {2017},
booktitle = {2017 IEEE Conference on Visual Analytics Science and Technology
  (VAST Challenge Entry)},
editor = {N.N.},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-030},
abstract = {We describe our approach to the analysis of 2017 VAST Challenge  Mini-Challenge 2 data. The challenge deals with readings from air  sampler stations. To answer the main question, the provenance of the  chemicals measured at the sampler stations, we extend the provided  data set by aggregated spatio-temporal provenance data. This data  is generated from the provided meteorological data and locations  map by using it as input for a particle tracer which calculates the  provenance of the particles arriving from the emitters (factories) at  the collectors (the locations of sampler stations). We use ComVis,  a coordinated multiple views (CMV) system, to analyze the whole  data set (the provided and generated data) by applying a sensor-  centric data model.},
}