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Extracting Region of Interest (ROI) details using LBS infrastructure and web-databases
S. Tiwari,
Published in
Pages: 376 - 379
The geographical areas that are considered to be popular and interesting are called Region of Interest (ROI). There are multiple sources that can be used for erecting the ROIs such as user trajectory, POI databases, internet news etc. A tourist spot, historical region, monuments, a forest reserve, city, state or country's administrative boundaries are considered as the ROI objects. The interesting facts of the regions can be used for on-the-spot infotainment (information + entertainment) while user is walking, driving or just sitting idle on the flight. One may want the information in different level of details based on the interests, travel direction, and speed. The best way to get the recent information is going through the internet news, discussion forums, and encyclopedias on the web. The challenge is that there is no universal database available that contains the region information along with the location based search capabilities. The maintaining the granularity of the information based on the size or the details of the region adds more challenges. The broader idea of our work is to use the existing LBS infrastructure to track the user and achieve other navigation objectives in the system. However, the freely available up-to-date internet infrastructure is used as the information source. Initially, the user location is determined by the conventional methods. In order to relate the location with the web content, we use the semantic labels associated to the underlying location. The semantic labels are fetched by using reverse geocoding that returns the local attractions, street city, state and country names etc. Then these labels are used to search the associated detailed content in the web. The region data is maintained in two forms i.e. locally populated database and the web databases. The locally populated database can be updated on the preconfigured time interval in order to avoid data staleness problem. We have used Wikipedia as the internet data source in our prototype. The further research is in progress extracting the ROI information from the POI databases with their spatial and non-spatial attributes. © 2012 IEEE.
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