In the maximizing range sum (MaxRS) problem, given (i) a set P of 2D points each of which is associated with a positive weight, and (ii) a rectangle r of specific extents, we need to decide where to place r in order to maximize the covered weight of r -that is, the total weight of the data points covered by r. Algorithms solving the problem exactly entail expensive CPU or I/O cost. In practice, exact answers are often not compulsory in a MaxRS application, where slight imprecision can often be comfortably tolerated, provided that approximate answers can be computed considerably faster. Motivated by this, the present paper studies the (1 − ǫ)-approximate MaxRS problem, which admits the same inputs as MaxRS, but aims instead to return a rectangle whose covered weight is at least (1 − ǫ)m ⋆ , where m ⋆ is the optimal covered weight, and ǫ can be an arbitrarily small constant between 0 and 1. We present fast algorithms that settle this problem with strong theoretical guarantees.
Tao et al. REF proposed a new problem called (1− )-approximate MaxRS which returns a solution that can be worse than optimal solution by a factor at most ; is an arbitrary small constant between 0 and 1.
Approximate MaxRS in Spatial Databases
PVLDB
PVLDB
Mathematics Computer Science