Spatial Data Ranking through Skyline Queries

Abstract- Spatial database systems manage large collections of geographic entities, which apart from spatial attributes contain nonspatial information (e.g., name, size, type, price, etc.). A spatial preference query(top- k queries) 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. The problem with top-k queries is that user must set weights for attributes and a function for aggregating them. This is hard for him in most cases. In this paper a new type of user preference queries called spatial nearest neighbor skyline queries will be introduced in which user has some sets of points as query parameters. For each point in database attributes are its distances to the nearest neighbors from each set of query points. By separating this query as a subset of dynamic skyline queries 22SN algorithm is provided for computing it