Ball Tree Data Structure for Location-Aware Market Data Management
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Solution Overview
Problem
Current systems for managing location-aware market data in transportation logistics are computationally inefficient, particularly when dealing with large numbers of growers and buyers, leading to high computational complexity and latency in determining optimal bids and freight costs.
Innovation Solution
The implementation of a decorated ball tree data structure that uses haversine distances and heuristic search methods to efficiently retrieve and rank location-dependent bids, reducing the computational load by pruning unnecessary searches and using in-memory data structures for fast retrieval of rate cards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current systems for managing location-aware market data are used, then bid ranking and freight cost calculations can be performed, but computational complexity and latency increase significantly when dealing with large numbers of growers and buyers
Solution Approach 1:
The patent segments the computational problem by organizing market data into hierarchical structures (ball trees) that divide the search space into manageable regions. This segmentation allows the system to process large numbers of growers and buyers by breaking down the overall bid ranking task into smaller, localized computations within each tree node, thereby reducing overall computational complexity while maintaining productivity.
Solution Approach 2:
The patent applies preliminary action by pre-processing and organizing market data into ball tree structures before actual bid ranking operations. This preliminary organization of location-aware data enables faster querying and reduces latency during runtime, as the heavy lifting of data structuring is completed in advance, allowing the system to handle large datasets efficiently when bid ranking is actually needed.
2Speed
If traditional data structures are used for storing market data, then data can be stored, but retrieval and processing of location-dependent bids becomes inefficient
Solution Approach 1:
The patent transitions from traditional flat data structures to multi-dimensional ball tree structures that organize data in spatial dimensions based on location. This dimensional transformation enables efficient retrieval of location-dependent bids by allowing the system to query and filter data along geographic dimensions, significantly improving retrieval speed and reducing processing latency compared to linear search methods.
Solution Approach 2:
The patent introduces ball tree data structures as an intermediary layer between raw market data and the bid ranking processing system. This intermediary structure pre-organizes location-aware data in a way that facilitates efficient querying, acting as a mediator that translates unstructured market data into a format optimized for rapid retrieval and processing, thereby reducing latency without losing any market data information.
3Measurement precision
If exhaustive search methods are used to determine optimal bids, then accurate results are achieved, but computational load and latency increase
Solution Approach 1:
The patent applies partial action by using ball tree structures to selectively process only the most relevant portions of the market data for each bid ranking query. Instead of exhaustively evaluating all possible combinations, the system performs partial computations focused on location-dependent regions identified by the ball trees, achieving sufficiently accurate results with significantly reduced calculation latency by avoiding unnecessary computations in irrelevant data regions.
Data Source
AI summary
A method of generating a visualization of location-dependent values is provided where a plurality of bids and a plurality of supply locations are received from at least one datastore. Each of the plurality of bids has an associated location. A first geographic location is selected from the plurality of supply locations. A ball tree is generated, the ball tree comprising a plurality of nodes, each node of the ball tree comprising a pivot geographic location and a radius, each node corresponding to at least one local value having a location within the radius of the pivot. The ball tree is traversed to generate sorted collection of location dependent values. Each of the location dependent values is determined from one of the plurality of bids. A map of the collection of location dependent values is output.