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We address the problem of maintaining the correct answer-sets to the <i>Conditional Maximizing Range-Sum</i> (C-MaxRS) query in spatial data streams. Given a set of (possibly weighted) 2D point objects, the traditional MaxRS problem determines an optimal placement for an axes-parallel rectangle <i>r</i> so that the number -- or, the weighted sum -- of objects in its interior is maximized. In many practical settings, the objects from a particular set -- e.g., restaurants -- can be of distinct types -- e.g., fast-food, Asian, etc. The C-MaxRS problem deals with maximizing the overall sum, given class-based existential constraints, i.e., a lower bound on the count of objects of interests from particular classes. We first propose an efficient algorithm to the static C-MaxRS query, and extend the solution to handle dynamic (data streams) settings. Our experiments over datasets of up to 100,000 objects show that the proposed solutions provide significant efficiency benefits.

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