Khái niệm cốt lõi
Optimizing geospatial video analytics workflows through Spatialyze's spatial-aware optimizations.
Tóm tắt
1. ABSTRACT
Geospatial videos are prevalent in daily life, lacking efficient data management systems.
Spatialyze framework introduced for end-to-end querying of geospatial videos.
Achieved up to 5.3× reduction in execution time with high accuracy.
2. INTRODUCTION
Rise of machine learning has made geospatial video analytics computationally intensive.
Lack of programming frameworks and data management systems hinders effective workflow specification.
3. USAGE SCENARIO
Data journalist example illustrates the ease of constructing analytic workflows using Spatialyze's S-Flow DSL.
Build-filter-observe paradigm simplifies workflow creation and execution.
4. CONSTRUCTING GEOSPATIAL VIDEO ANALYTIC WORKFLOWS
Conceptual data model includes World, Geographic Constructs, and Movable Objects.
S-Flow language enables users to interact with geospatial objects effectively.
5. WORKFLOW EXECUTION IN SPATIALYZE
Deferred execution optimizes workflow processing by understanding the entire workflow before execution.
Execution stages include Data Integrator, Video Processor, Movable Objects Query Engine, and Output Composer.
6. VIDEO PROCESSING OPTIMIZATION
Road Visibility Pruner: Utilizes geographic constructs' visibility to reduce frames processed by ML models.
Object Type Pruner: Filters out irrelevant objects based on user-defined object types for tracking optimization.
Thống kê
現実のオブジェクトの物理的な振る舞いを活用してワークフローの実行を最適化します。
Trích dẫn
"Videos shot using commodity hardware record metadata like time and location."
"Geospatial video analytics have become computationally intensive due to machine learning trends."