Speed Partitioning for Indexing Moving Objects

TL;DR

This paper introduces a formal, optimal speed partitioning method for indexing moving objects, significantly enhancing query performance by reducing search space expansion compared to heuristic approaches.

Contribution

It presents a novel, analytically derived speed partitioning technique using dynamic programming, outperforming existing heuristic-based methods in indexing moving objects.

Findings

Significant reduction in search space expansion.

Improved index query performance.

Outperforms state-of-the-art velocity-based partitioning methods.

Abstract

Indexing moving objects has been extensively studied in the past decades. Moving objects, such as vehicles and mobile device users, usually exhibit some patterns on their velocities, which can be utilized for velocity-based partitioning to improve performance of the indexes. Existing velocity-based partitioning techniques rely on some kinds of heuristics rather than analytically calculate the optimal solution. In this paper, we propose a novel speed partitioning technique based on a formal analysis over speed values of the moving objects. We first show that speed partitioning will significantly reduce the search space expansion which has direct impacts on query performance of the indexes. Next we formulate the optimal speed partitioning problem based on search space expansion analysis and then compute the optimal solution using dynamic programming. We then build the partitioned indexing system where queries are duplicated and processed in each index partition. Extensive experiments demonstrate that our method dramatically improves the performance of indexes for moving objects and outperforms other state-of-the-art velocity-based partitioning approaches.