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Heterogeneous stream processing for disaster detection and alarming

 
: Schnizler, F.; Liebig, T.; Mannor, S.; Souto, G.; Bothe, S.; Stange, H.

:

Lin, J. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE International Conference on Big Data, Big Data 2014. Vol.2 : Washington, DC, USA, 27 - 30 October 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-5667-8
ISBN: 978-1-4799-5665-4
ISBN: 978-1-4799-5666-1
S.914-923
International Conference on Big Data (BigData) <2, 2014, Washington/DC>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
We present a novel approach for event recognition in massive streams of heterogeneous data driven by privacy policies and big data event processing. New technologies in mobile computing combined with sensing infrastructures distributed in a city or country are generating massive, poly-structured spatio-temporal data. With a view on emergencies and disasters these various data sources enable early response and offer situative insights when integrated in an on-line incident recognition system. Our hereby presented system architecture integrates multi-faceted sensing and distributed event detection to identify, label and increase confidence in detected incidents. A higher flexibility than existing event detection approaches is achieved by combination of the data streams at a round table. At the round table the data flow adjusts itself during execution of the real-time detection system. This offers more robustness in case streams appear or disappear. The developed architecture is used in nation-wide and city-level incident recognition scenarios.

: http://publica.fraunhofer.de/dokumente/N-352524.html