Qualitative Representation of Spatial Knowledge
Qualitative Representation of Spatial Knowledge
Combining topological and size information for spatial reasoning
Artificial Intelligence
The House Is North of the River: Relative Localization of Extended Objects
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
Consistent Queries over Cardinal Directions Across Different Levels of Detail
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Modeling and Computing Ternary Projective Relations between Regions
IEEE Transactions on Knowledge and Data Engineering
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Towards Ontology-Based Formal Verification Methods for Context Aware Systems
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A size-based qualitative approach to the representation of spatial granularity
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Positions, regions, and clusters: strata of granularity in location modelling
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Granularity as a parameter of context
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
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Location-aware systems are mobile or spatially distributed computing systems, such as smart phones or sensor nodes in wireless sensor networks, enabled to react flexibly to changing environments. Due to severe restrictions of computational power on these platforms and real-time demands, most current solutions do not support advanced spatial reasoning. Qualitative Spatial Reasoning (QSR) and granularity are two mechanisms that have been suggested in order to make reasoning about spatial environments tractable. We propose an approach for combining these two techniques, so as to obtain a light-weight QSR mechanism, called partial order QSR (for brevity: PQSR), that is fast enough to allow application on small, low-cost computing devices. The key idea of PQSR is to use a core fragment of typical QSR relations, which can be expressed with partial orders and their linearizations, and to additionally delimit reasoning about these relations with a size-based granularity mechanism.