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Spatial database systems manage large collections of geographic entities, which apart from spatial attributes contain spatial information and non spatial information (e.g., name, size, type, price, etc.). An attractive type of preference queries, which select the best spatial location with respect to the quality of facilities in its spatial area. Given a set D of interesting objects (e.g., candidate locations), a top-k spatial preference query retrieves the k objects in D with the highest scores. The featured score of a given object is derived from the quality of features (e.g., location and nearby features) in its spatial neighborhood. For example, using a landed property agency database of flats for Sale, 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, bus stop, hospital, market, school, etc.) within their spatial neighborhood. This neighborhood concept can be defined by different functions by the user. It can be an explicit circular region within a given distance from the flat. Another sensitive definition is to assign higher rates to the features based on their proximity to the land. In this paper, we formally define spatial preference queries and propose suitable dynamic index techniques and searching algorithms for them. We extend [1] results with dynamic index structure in order to accommodate time - variant changes in the spatial data. In my current work is the top-k spatial preference query on road network, in which the distance between object and road is defined by their shortest path distance.

Inclusion Of Road Network In The Spatial Database For Features Searching Using Dynamic Index