Modeling and Querying Uncertain Spatio-Temporal Data
Abstract
Uncertain spatio-temporal object
The problem of modeling and managing uncertain data has received a great deal of interest, due to its manifold applications in spatial, temporal, multimedia and sensor databases. There exists a wide range of work covering spatial uncertainty in the static (snapshot) case, where only one point of time is considered. In contrast, the problem of modeling and querying uncertain spatio-temporal data has only been treated as a simple extension of the spatial case, disregarding time dependencies between consecutive timestamps. In this project, we present a framework for efficiently modeling and querying uncertain spatio-temporal data. The key idea of our approach is to model possible object trajectories by stochastic processes. This approach has three major advantages over previous work. First it allows answering queries in accordance with the possible worlds model. Second, dependencies between object locations at consecutive points in time are taken into account. And third it is possible to reduced all queries on this model to simple matrix multiplications. Based on these concepts we propose efficient solutions for different probabilistic spatio-temporal queries. In an experimental evaluation we show that our approaches are several order of magnitudes faster than state-of-the-art competitors.
Key Features
1. Uncertain spatio-temporal data is modeled by stochastic processes, specifically Markov-Chains
2. Three different queries are implemented on top of this model based on efficient matrix multiplications.
MATLAB Framework
In the scope of this project we developed a framework in matlab which can be downloaded and we would be happy to cooperate with interested contributors.
Download Matlab Code
Publications
 | T. Bernecker, L. Chen, T. Emrich, H.-P. Kriegel, N. Mamoulis, and A. Züfle. Managing Uncertain Spatio-Temporal Data. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data (QUeST), Chicago, Illinois, 2011.
|
| | T. Emrich, H.-P. Kriegel, N. Mamoulis, M. Renz, and A. Züfle. Querying uncertain spatio-temporal data. In Proceedings of the 28th International Conference on Data Engineering (ICDE), Washington, DC, 2012.
|
