Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the maximizing range sum (MaxRS) problem, given (i) a set P of 2D points each of which is associated with a positive ight, and (ii) a rectangle r of specific extents,  need to decide where to place r in order to maximize the covered ight of r - that is, the total ight of the data points covered by r. Algorithms solving the problem exactly entail expensive CPU or I/O cost. In practice, exact ansrs are often not compulsory in a MaxRS application, where slight imprecision can often be comfortably tolerated, provided that approximate ansrs can be computed considerably faster. Motivated by this, the present paper studies the (1 - e)-approximate MaxRS problem, which admits the same inputs as MaxRS, but aims instead to return a rectangle whose covered ight is at least (1-e)m*, where m* is the optimal covered ight, and e can be an arbitrarily small constant beten 0 and 1.  present fast algorithms that settle this problem with strong theoretical guarantees.
['Yufei Tao', 'Xiaocheng Hu', 'Dong-Wan Choi', 'Chin-Wan Chung']
I am writing a research paper on a idea Approximate MaxRS in spatial databases
[{"from": "human", "value": "I am writing a research paper on a idea Approximate MaxRS in spatial databases"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the maximizing range sum (MaxRS) problem, given (i) a set P of 2D points each of which is associated with a positive ight, and (ii) a rectangle r of specific extents,  need to decide where to place r in order to maximize the covered ight of r - that is, the total ight of the data points covered by r. Algorithms solving the problem exactly entail expensive CPU or I/O cost. In practice, exact ansrs are often not compulsory in a MaxRS application, where slight imprecision can often be comfortably tolerated, provided that approximate ansrs can be computed considerably faster. Motivated by this, the present paper studies the (1 - e)-approximate MaxRS problem, which admits the same inputs as MaxRS, but aims instead to return a rectangle whose covered ight is at least (1-e)m*, where m* is the optimal covered ight, and e can be an arbitrarily small constant beten 0 and 1.  present fast algorithms that settle this problem with strong theoretical guarantees."}]