Geospatial Dataset Distribution via Low-Discrepancy Sequences
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Solution Overview
Problem
The oil and gas exploration industry faces challenges in efficiently processing large geospatial datasets due to a lack of suitable load-balancing strategies, which hinders interactive analysis and scalability in seismic and electromagnetic surveys.
Innovation Solution
A method involving distributing geospatial data objects across a multi-dimensional grid of points using a low-discrepancy sequence, where the points are assigned to computing units based on sub-intervals generated by a quasi-random generator, ensuring load-balanced distribution and efficient processing across multiple computing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If geospatial datasets are stored in distributed compute nodes due to limited RAM capacity, then the system can handle large datasets exceeding single-node memory, but load imbalance occurs across computing units reducing processing efficiency
Solution Approach 1:
The patent segments the geospatial dataset into multiple partitions distributed across N computing units. Each computing unit processes a specific partition, enabling parallel processing of large datasets that exceed single-node memory capacity. The segmentation is achieved by dividing the multi-dimensional grid into manageable chunks that can be independently processed.
Solution Approach 2:
The patent applies local quality by assigning different data partitions to different computing units based on their processing capabilities and current load. Each computing unit handles data locally stored in its memory, reducing inter-node communication overhead. The low-discrepancy sequence ensures that each computing unit receives a representative sample of the overall dataset distribution.
2Ease of operation
If traditional random distribution methods are used to allocate data objects across computing units, then data distribution appears uniform, but actual load balance is poor leading to some computing units being underutilized
Solution Approach 1:
The patent implements dynamic load balancing by continuously monitoring the processing status and data distribution across computing units. The system dynamically adjusts data allocation to ensure optimal utilization, moving data objects from overloaded units to underutilized ones based on real-time conditions rather than static random assignment.
Solution Approach 2:
The patent incorporates feedback mechanisms where computing units report their processing status and data distribution metrics to a central coordinator. Based on this feedback, the system recalculates and redistributes data objects to achieve better load balance, ensuring that all computing units are utilized efficiently throughout the processing operation.
3Speed
If geospatial datasets are loaded into distributed RAM across multiple nodes, then interactive analysis speed improves compared to disk-based storage, but system complexity increases due to distributed memory management
Solution Approach 1:
The patent implements a universal data structure and communication protocol that works across all computing units in the distributed system. This multi-functional approach allows the same processing logic to be applied uniformly across different nodes, simplifying the management complexity despite the distributed architecture. The system handles both data processing and memory management using standardized interfaces.
4Ease of manufacture
If computing units are assigned data objects based on simple range partitioning, then implementation is straightforward, but data objects with similar spatial relationships may be scattered across different computing units
Solution Approach 1:
The patent transforms the multi-dimensional spatial coordinates of geospatial data objects into a one-dimensional ordering using a low-discrepancy sequence. This dimensional transformation preserves spatial relationships while enabling efficient distribution across computing units. Data objects that are close in the original multi-dimensional space remain close in the transformed one-dimensional sequence, maintaining spatial coherence during distribution.
Data Source
AI summary
Data objects of a geospatial data set are arranged in a low-discrepancy sequence spanning over a pre-defined interval, and assigned to N computing units based on in which sub-interval within the pre-defined interval the point, to which the data object belongs, falls. A subset of the data objects that have been distributed over the N computing units is subjected to processing operations by computer readable instructions loaded on each of the N computing units.