Abstract
Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial) road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm.
Highlights
With the widespread use of mobile computing devices [1,2,3,4,5,6,7], location-based services [8] have attracted much attention as one of the most promising applications whose main functionality is to process location-related queries on spatial databases
Given a set of positive-weighted points and a query rectangle r of a given size, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the sum of the weights of all the points covered by r is maximized
Because this is the first work for processing MaxRS queries in a road network database, we develop a naive algorithm to compare with our proposed algorithm
Summary
With the widespread use of mobile computing devices [1,2,3,4,5,6,7], location-based services [8] have attracted much attention as one of the most promising applications whose main functionality is to process location-related queries on spatial databases. We can see this significant difference, where the Euclidean distance between f2 and f4 is about 1.24, while for moving from f2 to f4 in real-life, we must pass through V5 and V4 with total length around 3.74, which is three times farther than Euclidean distance With this problem in mind, we study, for the first time to the best of our knowledge, the problem of processing MaxRS queries in a road network, where the distance between two points is determined by the length of the shortest path connecting them (i.e., network distance [13]). The total weight of all the facilities whose network distance to all points of stage s is less than or equals 1.5 is 3, which is maximum in this scenario.
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