An Advanced Discrete Model for Uncertain Spatial Data

Tøssebro, Erlend; Nygård, Mads

doi:10.1007/3-540-45703-8_4

Cited by 17 publications

(17 citation statements)

References 7 publications

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“…Using this model, the notion of fuzzy point, fuzzy line and fuzzy region may be defined in R 2 . A similar modelling has been proposed in Tøssebro and Nygård (2002) and in Worboys and Clementini (2001). This process facilitates the exploitation of fuzzy objects in a GIS and maintains the interest of the valued confidence contribution.…”

Section: About Modelling Imprecise Spatial Informationmentioning

confidence: 97%

F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

Zoghlami¹,

Runz²,

Akdag³

2016

IJSTMIS

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In the literature, several studies have focused on introducing fuzzy extensions to the relational and/or object database models in order to store the imprecision. Indeed, on one hand, fuzzy EER and fuzzy UML are both applied for fuzzy object-oriented database modelling. On the other hand, Fuzzy ER is adapted for fuzzy relational database models. All these previous fuzzy conceptual modelling methods are not adapted to fuzzy spatiotemporal data. In this paper, we propose an approach for modelling imprecise data in object and relational databases based on the representation of data using connected and normalised fuzzy sets stored via α-cuts. The approach is applied to geographical information systems in order to handle imprecise spatiotemporal data.Keywords: imprecise data; fuzzy set; geographical information system; GIS; spatiotemporal data; unified modelling language; UML.Reference to this paper should be made as follows: Zoghlami, A., de Runz, C. and Akdag, H. (2016)

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Section: About Modelling Imprecise Spatial Informationmentioning

confidence: 97%

F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

Zoghlami¹,

Runz²,

Akdag³

2016

IJSTMIS

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“…For the other uncertain data types we use a possible area with a probability function as proposed in [23] and also used in [2]. The unint and unreal 2 are represented as a finite set of disjoint, non-adjacent intervals (range [18]) of int and real types respectively, assuming the uniform probability distribution function.…”

Section: Data Typesmentioning

confidence: 99%

“…The advantage of representing these data types as described here instead of as in [23,25] is that no storage for the probability distribution function is necessary, because uniformity is assumed. Moreover, assuming uniformity, the computation becomes simpler.…”

Section: Data Typesmentioning

confidence: 99%

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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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“…Several models have been presented for storing spatial data with uncertain or vague positions, including the models from Nygård 2002a and2002b) that this paper builds on. However, these models do not handle topological relationships well.…”

Section: Introductionmentioning

confidence: 99%