FurMoLi: A Future Query Technique for Moving Objects Based on a Learned Index

Jiwei Yang, Chong Zhang (), Wen Tang, Bin Ge, Hongbin Huang and Shiyu Yang
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Jiwei Yang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410000, China
Chong Zhang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410000, China
Wen Tang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410000, China
Bin Ge: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410000, China
Hongbin Huang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410000, China
Shiyu Yang: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 13, 1-21

Abstract: The future query of moving objects involves predicting their future positions based on their current locations and velocities to determine whether they will appear in a specified area. This technique is crucial for positioning and navigation, and its importance in our daily lives has become increasingly evident in recent years. Nonetheless, the growing volume of data renders traditional index structures for moving objects, such as the time-parameterized R-tree (TPR-tree), inefficient due to their substantial storage overhead and high query costs. Recent advancements in learned indexes have demonstrated a capacity to significantly reduce storage overhead and enhance query efficiency. However, most existing research primarily addresses static data, leaving a gap in the context of future queries for moving objects. We propose a novel f uture q u e r y technique for m oving o bjects based on a l earned i ndex ( FurMoLi for short). FurMoLi encompasses four key stages: firstly, a data partition through clustering based on velocity and position information; secondly, a dimensionality reduction mapping two-dimensional data to one dimension; thirdly, the construction of a learned index utilizing piecewise regression functions; and finally, the execution of a future range query and future KNN query leveraging the established learned index. The experimental results demonstrate that FurMoLi requires 4 orders of magnitude less storage overhead than TPR-tree and 5 orders of magnitude less than B + -tree for moving objects ( B x -tree). Additionally, the future range query time is reduced to just 41.6% of that for TPR-tree and 34.7% of that for B x -tree. For future KNN queries, FurMoLi’s query time is only 70.1% of that for TPR-tree and 47.4% of that for B x -tree.

Keywords: moving objects; machine learning; learned index; clustering; future range query; future KNN query (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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