Uncertainty in Spatiotemporal Databases

Tøssebro, Erlend; Nygård, Mads

doi:10.1007/3-540-36077-8_5

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…Another model for representing uncertainty in spatial database is introduced by Tossebro et al in [22][23][24][25][26]. In [23] the authors propose a representation of spatial data through uncertain points, uncertain lines and uncertain regions.…”

Section: Related Workmentioning

confidence: 98%

“…In [25] this model is refined in order to reduce the storage space required and to simplify the computation of the core and support regions. In [24] the authors extend their model with some constructs for representing also temporal uncertainty into a spatial database. Finally, in [26] the model is completed with the representation of topological relationships between uncertain spatial objects, since they cannot be directly inferred from the object representations.…”

Section: Related Workmentioning

confidence: 99%

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A framework for integrating multi-accuracy spatial data in geographical applications

Belussi

Migliorini

2011

Geoinformatica

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In recent years the integration of spatial data coming from different sources has become a crucial issue for many geographical applications, especially in the process of building and maintaining a Spatial Data Infrastructure (SDI). In such context new methodologies are necessary in order to acquire and update spatial datasets by collecting new measurements from different sources. The traditional approach implemented in GIS systems for updating spatial data does not usually consider the accuracy of these data, but just replaces the old geometries with the new ones. The application of such approach in the case of an SDI, where continuous and incremental updates occur, will lead very soon to an inconsistent spatial dataset with respect to spatial relations and relative distances among objects. This paper addresses such problem and proposes a framework for representing multi-accuracy spatial databases, based on a statistical representation of the objects geometry, together with a method for the incremental and consistent update of the objects, that applies a customized version of the Kalman filter. Moreover, the framework considers also the spatial relations among objects, since they represent a particular kind of observation that could be derived from geometries or be observed independently in the real world. Spatial relations among objects need also to be compared in spatial data integration and we show that they are necessary in order to obtain a correct result in merging objects geometries.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

A framework for integrating multi-accuracy spatial data in geographical applications

Belussi

Migliorini

2011

Geoinformatica

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“…In addition, another state-of-the-art Stochastic Differential Equation (SDE) model imitated the system diffusion and captured both epistemic and aleatoric uncertainty by involving the Brownian motion [16]. Specifically, for spatiotemporal uncertainty, [28] first proposed the concept of uncertainty in spatiotemporal databases, and then researchers have started to explore the uncertainties in large-scale climate datasets [24,29], as well as uncertainty in numerical weather forecasting [22,30] with BNN methods. These works were the beginning where uncertainty quantification was introduced into spatiotemporal data mining, but they failed to capture the spatial dependency and temporal evolution of uncertainties.…”

Section: Literature Reviewmentioning

confidence: 99%

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

Zhou,

Wang,

Xie

et al. 2021

Preprint

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The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests. Given the fact that uncertainty quantifies the potential variations around prediction results, traditional learning schemes always lack uncertainty labels, and conventional uncertainty quantification approaches mostly rely upon statistical estimations with Bayesian Neural Networks or ensemble methods. However, they have never involved any spatiotemporal evolution of uncertainties under various contexts, and also have kept suffering from the poor efficiency of statistical uncertainty estimation while training models with multiple times. To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. To address the issue of lacking labels of uncertainty, we propose a hierarchical data turbulence scheme where we can actively inject controllable uncertainty for guidance, and hence provide insights to both uncertainty quantification and weak supervised learning. Finally, we re-calibrate and boost the prediction performance by devising a gated-based bridge to adaptively leverage the learned uncertainty into predictions. Extensive experiments on three real-world spatiotemporal mobility sets have corroborated the superiority of our proposed model in terms of both forecasting and uncertainty quantification.

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“…For the unbool we use the same approach as in [24,10] where the maybe value is added. The carrier set for the unbool data type is…”

Section: Data Typesmentioning

confidence: 99%

“…The works in [24,23,25] propose extensions to the model in [18] to handle uncertainty. Probability density functions are used to model the uncertain positions of moving objects, which can be points, lines, and regions from [18].…”

Section: Introductionmentioning

confidence: 99%

Supporting uncertainty in moving objects in network databases

Almeida

Güting

2005

Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems

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The management of moving objects has been intensively studied in the recent years. A wide and increasing range of database applications has to deal with spatial objects whose position changes continuously over time, called moving objects. Due to the continuous and unpredictable nature of the movements, they cannot be precisely stored in a database, and therefore objects' positions are sampled, and between these sampled positions interpolation is used. This sampling/interpolation approach results in uncertainty in the objects' positions in the whole trajectory of the moving objects. In this paper, we try to analyze this problem about uncertainty when the movement is restricted to a network. Examples of such movements are cars in highways and trains in railroads. The uncertainty problem is simpler in such cases compared to the free movement in 2-dimensional space. We describe the geometry of the uncertain trajectories of the objects with movement constrained to networks, an extension to the framework in [18,16] to support uncertainty, as well as some implementation considerations using Secondo, an extensible database system that supports nonstandard applications.

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Uncertainty in Spatiotemporal Databases

Cited by 20 publications

References 7 publications

A framework for integrating multi-accuracy spatial data in geographical applications

A framework for integrating multi-accuracy spatial data in geographical applications

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

Supporting uncertainty in moving objects in network databases

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