Composing Cardinal Direction Relations

Skiadopoulos, Spiros; Koubarakis, Manolis

doi:10.1007/3-540-47724-1_16

Cited by 74 publications

(136 citation statements)

References 10 publications

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“…Static cardinal directions like north and northeast represent important qualitative spatial relationships that describe relative direction positions between static spatial objects. Many models follow a projection-based approach, where direction relationships are defined using projection lines orthogonal to the coordinate axes [8,9]. Some models apply a cone-based approach that defines direction relations by using angular zones [10,11].…”

Section: Related Workmentioning

confidence: 99%

Computing the Cardinal Direction Development between Moving Points in Spatio-temporal Databases

Chen

Liu

Schneider

2011

Advances in Spatial and Temporal Databases

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Abstract. In the same way as moving objects can change their location over time, the spatial relationships between them can change over time. An important class of spatial relationships are cardinal directions like north and southeast. In spatial databases and GIS, they characterize the relative directional position between static objects in space and are frequently used as selection and join criteria in spatial queries. Transferred to a spatiotemporal context, the simultaneous location change of different moving objects can imply a temporal evolution of their directional relationships, called development. In this paper, we provide an algorithmic solution for determining such a temporal development of cardinal directions between two moving points. Based on the slice representation of moving points, our solution consists of three phases, the time-synchronized interval refinement phase for synchronizing the time intervals of two moving points, the slice unit direction evaluation phase for computing the cardinal directions between two slice units that are defined in the same time interval from both moving points, and finally the direction composition phase for composing the cardinal directions computed from each slice unit pair. Finally, we show the integration of spatio-temporal cardinal directions into spatio-temporal queries as spatio-temporal directional predicates, and present a case study on the hurricane data.

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Section: Related Workmentioning

confidence: 99%

Computing the Cardinal Direction Development between Moving Points in Spatio-temporal Databases

Chen

Liu

Schneider

2011

Advances in Spatial and Temporal Databases

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“…The projection-based models define direction relations by using partitioning lines parallel to the coordinate axes. The Direction-Relation Matrix model [7,8] helps capture the influence of the objects' shapes as shown in Figure 1c. However, this model only applies to spatial objects with non-temporal variations.…”

Section: Related Workmentioning

confidence: 99%

The Objects Interaction Graticule for Cardinal Direction Querying in Moving Objects Data Warehouses

Viswanathan

Schneider

2010

Advances in Databases and Information Systems

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Abstract. Cardinal directions have turned out to be very important qualitative spatial relations due to their numerous applications in spatial wayfinding, GIS, qualitative spatial reasoning and in domains such as cognitive sciences, AI and robotics. They are frequently used as selection criteria in spatial queries. Moving objects data warehouses can help to analyze complex multidimensional data of a spatio-temporal nature and to provide decision support. However, currently there is no available method to query for cardinal directions between spatio-temporal objects in data warehouses. In this paper, we introduce the concept of a moving objects data warehouse (MODW) for storing and querying multidimensional spatio-temporal data. Further, we also present a novel two-phase approach to model and query for cardinal directions between moving objects by using the MODW framework. First, we apply a tiling strategy that determines the zone belonging to the nine cardinal directions of each spatial object at a particular time and then intersects them. This leads to a collection of grids over time called the Objects Interaction Graticule (OIG). For each grid cell, the information about the spatial objects that intersect it is stored in an Objects Interaction Matrix. In the second phase, an interpretation method is applied to these matrices to determine the cardinal direction between the moving objects. These results are integrated into MDX queries using directional predicates.

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“…Typically used spatial calculi tend to focus on one dimension (e.g. Region Connection Calculus (RCC) (Randell et al, 1992) on topology and Cardinal Direction Calculus (CDC) (Goyal, 2000;Skiadopoulos and Koubarakis, 2004) on orientation). Thus, the combination of qualitative spatial calculi of different dimensions is necessary.…”

Section: Introductionmentioning

confidence: 99%

Supporting Mobile Robot’s Tasks through Qualitative Spatial Reasoning

2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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Abstract:In this paper, we present an application of qualitative spatial reasoning technologies for supporting mobile robot tasks. While focusing on detection of interaction ability, we provide a combination of the spatial reasoning calculi RCC-8 and CDC as well as their integration with OWL-based ontologies. An architecture that uses Prolog and complex-event processing implements our approach. We illustrate the results with a mobile robot scenario in a restaurant.

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Composing Cardinal Direction Relations

Cited by 74 publications

References 10 publications

Computing the Cardinal Direction Development between Moving Points in Spatio-temporal Databases

Computing the Cardinal Direction Development between Moving Points in Spatio-temporal Databases

The Objects Interaction Graticule for Cardinal Direction Querying in Moving Objects Data Warehouses

Supporting Mobile Robot’s Tasks through Qualitative Spatial Reasoning

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