Towards Managing Uncertain Spatial Information for Situational Awareness Applications.

Appeared in IEEE TKDE 2008 Journal, Vol. 20(10), October 2008

Yiming Ma, Dmitri V. Kalashnikov, and Sharad Mehrotra.

Computer Science Department
University of California, Irvine


Situational awareness (SA) applications monitor the real world and the entities therein to support tasks such as rapid decision-making, reasoning, and analysis. Raw input about unfolding events may arrive from variety of sources in the form of sensor data, video streams, human observations, and so on, from which events of interest are extracted. Location is one of the most important attributes of events, useful for a variety of SA tasks. In this article, we consider the problem of reaching situation awareness from textual input. We propose an approach to probabilistically model and represent (potentially uncertain) event locations described by human reporters in the form of free text. We analyze several types of spatial queries of interest in SA applications. We design techniques to store and index the uncertain locations, to support the efficient processing of queries. Our extensive experimental evaluation over real and synthetic datasets demonstrates the effectiveness and efficiency of our approach.

Categories and Subject Descriptors:

H.2.8 [Information System]: Database Management—Spatial databases and GIS


Location Disambiguation, Uncertain Location Modeling, Probabilistic Indexing, Probabilistic Representation, Histogram Compression.

Downloadable files:

Paper: TKDE08_dvk_SAT.pdf

BibTeX entry:

   author    = {Yiming Ma and Dmitri V.\ Kalashnikov and Sharad Mehrotra},
   title     = {Towards Managing Uncertain Spatial Information for Situational Awareness Applications},
   journal   = {{IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)}},
   year      = 2008,
   volume    = 20,
   number    = 10,
   month     = oct,
   year      = 2008
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