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Abstract

In order to deal with space computationally, it has to be represented in a standardized way such that it can be processed by a computer. In this regard, we distinguish between the quantitative and qualitative representation of space, which are discussed in detail in the Sections 3.1 and 3.2, respectively. While the former deals with concrete facts about the spatial properties of an artifact using numerical values (as provided by sensors for example), the latter is concerned with symbolic and human-readable abstractions of spatial relations (i.e. symbols are used for the representation rather than numeric values) which are acquired by a pairwise comparison of quantitative spatial properties. For the representation of an artifact’s spatial properties we propose to use so-called Zones-of-Influence, which are explicitly defined geographical regions that are relevant for the application. Our focus is on qualitative spatial relationships as well as their changes over time and the application-dependent semantics. We consider qualitative relationship abstractions to be particularly valuable for implementing services that are distributed among multiple artifacts in physical space. Quantitative spatial properties and qualitative spatial relations – which together constitute an artifact’s spatial context – are exchanged between artifacts in range by means of XML-based self-descriptions, which enables them to reason about both self-determined and received relations; an example structure of self-descriptions, which we used for evaluation purposes in this thesis, is presented in Section 3.3. Section 3.4 eventually sums up findings concerning the quantitative and qualitative representation of spatial properties and relations with regard to their representation and exchange by autonomous embedded systems.

Keywords

Spatial Relation Anchor Point Reference Object Spatial Property Qualitative Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Vieweg+Teubner | GWV Fachverlage GmbH 2009

Authors and Affiliations

  • Clemens Holzmann

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