:: Volume 2, Issue 1 (6-2014) ::
jgit 2014, 2(1): 1-15 Back to browse issues page
Context-Aware Network Generalization for Optimum-Path Analysis
Mahdi Rahimi *, Mohammad Reza Malek
K.N.Toosi University of Technology
Abstract:   (3972 Views)
Generalization is a prevalent concept in Cartography to which has been added new aspects such as model generalization with developments in GIS. Increasing demand for tailored and ubiquitous geospatial services like wayfinding, makes the context-aware generalization a noticeable research area in GIScience. Most of the wayfinding services use the network data model as the main spatial data model for their analyses. Whatever data or information that characterizes the situations relevant to users, systems and applications, can be considered as context. A context-aware service is as a service which can sense user, environment and device’s situations and respond to user requests concerning contexts to fulfill the user’s needs better. From the GI services perspective, the context of a query could be the location of the device, environmental settings that query is made in, the time, the activity of the user, user’s personal information, the user’s favorites and information needs, the user’s cognitive map of environment, the mode of travel, the purpose of travel and the device’s technological specifications. In this paper, we try to propose and implement a method for context-aware network generalization at the analysis level. For finding the best path, user contexts like user’s favorite streets will be used. These contexts are modeled as edge’s attributes and those edges which fulfill user’s needs, will be the generators of the Network Voronoi Diagram. With these diagrams, the network will be simplified into sub graphs using Delaunay diagram over the network. The path would be composed of the path between origin and destination to their corresponding generators and the path between generators. This method guarantees the maximum use of edges with user’s need context as well as decreasing computational cost.
Keywords: Context-Awareness, Network Generalization, Network Pruning, Network Voronoi Diagram, Sub-Network.
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Type of Study: Research |
Received: 2015/07/11 | Accepted: 2015/07/11 | Published: 2015/07/11

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