Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
arXiv:2306.12857v3 Announce Type: replace Abstract: The storage, management, and application of massive spatio-temporal data are widely used in practical scenarios, including public safety. However, due to the unique spatio-temporal distribution characteristics of real-world data, existing methods still face limitations in preserving spatio-temporal proximity and achieving load balancing in distributed storage. This paper proposes an efficient partitioning method for large-scale public safety spatio-temporal data based on information loss constraints, named IFL-LSTP. The model combines a spati
The proliferation of massive spatio-temporal data, especially in public safety applications, is driving the urgent need for more efficient and robust data management solutions to handle increasing data volumes and complexity.
Efficient partitioning of large-scale spatio-temporal data is critical for extracting actionable insights, improving response times in public safety, and managing the computational burden of AI applications on such datasets.
This method offers a more optimized way to store and retrieve geographically and temporally sensitive data, potentially improving the performance and reliability of systems dependent on such information, particularly in public safety contexts.
- · Public safety agencies
- · Smart city initiatives
- · Distributed database providers
- · AI/ML companies specializing in spatio-temporal data
- · Organizations relying on inefficient data partitioning methods
- · Legacy data storage solutions
Improved real-time analysis capabilities for large-scale public safety events.
Enhanced development and deployment of AI models that require accurate and timely spatio-temporal data, leading to better predictive policing or emergency response.
The development of a new standard for spatio-temporal data management within critical infrastructure, potentially influencing future regulatory frameworks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG