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We present a system for efficient detection, continuous maintenance and visualization of range-constrained optimal density clusters of moving objects trajectories, a.k.a. Continuous Maximizing Range Sum (Co-MaxRS) queries. Co-MaxRS is useful in any domain involving continuous detection of “most interesting” regions involving mobile entities (e.g., traffic monitoring, environmental tracking, etc.). Traditional MaxRS finds a location of a given rectangle R which maximizes the sum of the weighted-points (objects) in its interior. Since moving objects continuously change their locations, the MaxRS at a particular time instant need not be a solution at another time instant. Our system solves two important problems: (1) Efficiently computing Co-MaxRS answer-set; and (2) Visualizing the results. This demo will present the implementation of our efficient pruning schemes and compact data structures, and illustrate the end-user tools for specifying the parameters and selecting datasets for Co-MaxRS, along with visualization of the optimal locations.more » « less
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We address the problem of maintaining the correct answer-sets to the Conditional Maximizing Range-Sum (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 r 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.more » « less
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We address the problem of efficient maintenance of the answer to a new type of query: Continuous Maximizing Range- Sum (Co-MaxRS) for moving objects trajectories. The traditional static/spatial MaxRS problem finds a location for placing the centroid of a given (axes-parallel) rectangle R so that the sum of the weights of the point-objects from a given set O inside the interior of R is maximized. However, moving objects continuously change their locations over time, so the MaxRS solution for a particular time instant need not be a solution at another time instant. In this paper, we devise the conditions under which a particular MaxRS solution may cease to be valid and a new optimal location for the query-rectangle R is needed. More specifically, we solve the problem of maintaining the trajectory of the centroid of R. In addition, we propose efficient pruning strategies (and corresponding data structures) to speed-up the process of maintaining the accuracy of the Co-MaxRS solution. We prove the correctness of our approach and present experimental evaluations over both real and synthetic datasets, demonstrating the benefits of the proposed methods.more » « less
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We address the problem of in-network processing of k-Maximizing Range Sum (k-MaxRS) queries inWireless Sensor Networks (WSN). The traditional, Computational Geometry version of the MaxRS problem considers the setting in which, given a set of (possibly weighted) 2D points, the goal is to determine the optimal location for a given (axes-parallel) rectangle R to be placed so that the sum of the weights (or, a simple count) of the input points in R’s interior is maximized. In WSN, this corresponds to finding the location of region R such that the sum of the sensors’ readings inside R is maximized. The k-MaxRS problem deals with maximizing the overall sum over k such rectangular regions. Since centralized processing – i.e., transmitting the raw readings and subsequently determining the k-MaxRS in a dedicated sink – incur communication overheads, we devised an efficient distributed algorithm for in-network computation of k-MaxRS. Our experimental observations show that the novel algorithm provides significant energy/communication savings when compared to the centralized approach.more » « less