Evaluating the Impact Of Spatial Features Of Mobility Data and Index Choice On Database Performance

2025-05-20

AI Summary

This work addresses the performance optimization challenge of spatial databases in mobile Internet-of-Things (IoT) environments. We systematically investigate the coupled effects among spatial indexing strategies (e.g., R-tree, GiST), mobile object data representations (trajectory vs. snapshot), and intrinsic spatial characteristics of datasets—specifically, geographic overlap degree and distribution skewness. We propose, for the first time, approximate quantification methods for overlap and skewness, and design a comprehensive PostGIS benchmark covering diverse real-world datasets and mixed read/write workloads. Experimental evaluation uncovers scenario-specific optimal combinations of index structures and data formats under varying spatial characteristics. The results yield actionable, deployment-ready guidelines for storage layout and query optimization in spatiotemporal applications; performance differences for key operations reach up to 5.3× across configurations.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The growing number of moving Internet-of-Things (IoT) devices has led to a surge in moving object data, powering applications such as traffic routing, hotspot detection, or weather forecasting. When managing such data, spatial database systems offer various index options and data formats, e.g., point-based or trajectory-based. Likewise, dataset characteristics such as geographic overlap and skew can vary significantly. All three significantly affect database performance. While this has been studied in existing papers, none of them explore the effects and trade-offs resulting from a combination of all three aspects. In this paper, we evaluate the performance impact of index choice, data format, and dataset characteristics on a popular spatial database system, PostGIS. We focus on two aspects of dataset characteristics, the degree of overlap and the degree of skew, and propose novel approximation methods to determine these features. We design a benchmark that compares a variety of spatial indexing strategies and data formats, while also considering the impact of dataset characteristics on database performance. We include a variety of real-world and synthetic datasets, write operations, and read queries to cover a broad range of scenarios that might occur during application runtime. Our results offer practical guidance for developers looking to optimize spatial storage and querying, while also providing insights into dataset characteristics and their impact on database performance.

Problem

• Evaluating impact of spatial data features on database performance
• Analyzing effects of index choice and data format combinations
• Assessing dataset characteristics like overlap and skew influence

Innovation

• Evaluates PostGIS performance with spatial indexes
• Proposes novel overlap and skew approximation methods
• Benchmarks indexing strategies and data formats impact