Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints

TL;DR

This paper introduces an efficient partitioning method for large-scale public safety spatio-temporal data that balances load and maintains data proximity, improving storage and management efficiency.

Contribution

The proposed IFL-LSTP method combines spatio-temporal and graph partitioning to reduce data scale while ensuring load balancing and proximity preservation.

Findings

Significantly reduces data scale while maintaining accuracy

Ensures load balancing in distributed storage systems

Demonstrates superior performance on real-world datasets

Abstract

The storage, management, and application of massive spatio-temporal data are widely applied in various practical scenarios, including public safety. However, due to the unique spatio-temporal distribution characteristics of re-al-world data, most existing methods have limitations in terms of the spatio-temporal proximity of data and load balancing in distributed storage. There-fore, this paper proposes an efficient partitioning method of large-scale public safety spatio-temporal data based on information loss constraints (IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal point da-ta by combining the spatio-temporal partitioning module (STPM) with the graph partitioning module (GPM). This approach can significantly reduce the scale of data while maintaining the model's accuracy, in order to improve the partitioning efficiency. It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results. This method provides a new solution for distributed storage of mas-sive spatio-temporal data. The experimental results on multiple real-world da-tasets demonstrate the effectiveness and superiority of IFL-LSTP.