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.
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Maximum Range-Sum for Dynamically Occurring Objects with Decaying Weights
This work addresses a novel variant of the Maximum Range-Sum (MaxRS) query for settings in which spatial point objects occur dynamically and, upon occurrence, their significance (i.e., weight) decays over time. The objective of the original MaxRS query is to find a location to place (the centroid of) a fixed-size spatial rectangle so that the sum of the weights of the point objects in its interior is maximized. The unique aspect of the problem studied in this paper, which we call DDW-MaxRS (Dynamic and Decaying Weights MaxRS), is that the placement of its solution can vary over time due to the joint impact of the arrival of new objects and the change of the corresponding weights of the existing objects over time. To improve the efficiency of the DDW-MaxRS problem processing, we propose a memory-efficient approximate algorithmic solution that will naturally infuse uncertainty in the answer. We formally analyze the error bounds’ properties and provide experimental results to quantify the effectiveness of the proposed approach.
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- Award ID(s):
- 2030249
- NSF-PAR ID:
- 10403313
- Date Published:
- Journal Name:
- Advances in Databases and Information Systems (ADBIS) 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
