Ranking Spatial Data by Quality Preferences - 2011 IEEE Projects
Man Lung Yiu, Hua Lu, Nikos Mamoulis, and Michail Vaitis
Abstract :
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood.
For example,
using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to consider the whole spatial domain and assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensively evaluation of our methods on both real and synthetic data reveal that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.
Download Base Paper: Click Here
The project will be taken and process on going Status will be uploaded Quickly...
Man Lung Yiu, Hua Lu, Nikos Mamoulis, and Michail Vaitis
Abstract :
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood.
For example,
using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to consider the whole spatial domain and assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensively evaluation of our methods on both real and synthetic data reveal that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.
Download Base Paper: Click Here
The project will be taken and process on going Status will be uploaded Quickly...
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