Context-Based Map Data Retrieval Using Hierarchical Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing tile-based map data retrieval methods are inefficient due to the retrieval of unnecessary data, leading to wastage of bandwidth and resources, and the frequent rebalancing required in tree-based data structures to maintain efficient data retrieval, which does not consider contextual information.
Innovation Solution
The method processes contextual information to determine a restricted range of location information relevant to a query, combined with sensor data to filter map data, using a neural network to predict contextually relevant data, thereby optimizing data retrieval and eliminating the need for data structure rebalancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tile-based map data retrieval is used, then map data can be organized for efficient retrieval, but retrieving a specific map data becomes inefficient because a single map tile may contain more or less map data than actually needed, wasting bandwidth and memory
Solution Approach 1:
The patent segments map data at a finer granularity than traditional tile-based approaches by using hierarchical encoding where map data is divided into super-blocks that can be further divided into blocks and sub-blocks. This allows retrieval of only the specific portions needed rather than entire tiles, resolving the contradiction between retrieval efficiency and resource waste.
Solution Approach 2:
The patent applies local quality by encoding different portions of map data with different levels of detail and priority. Frequently accessed or important map data regions are maintained with higher precision and accessibility, while less critical regions use compressed representations. This allows the system to optimize bandwidth and memory usage by allocating resources based on local data importance rather than uniform tile structures.
2Productivity
If tree-based data structure is implemented to store map data, then efficient data retrieval can be achieved, but frequent rebalancing and rehashing are required to maintain efficient retrieval
Solution Approach 1:
The patent implements a dynamic hierarchical data structure that automatically adapts to data access patterns without requiring manual rebalancing. The structure dynamically creates or merges super-blocks, blocks, and sub-blocks based on access frequency and spatial relationships, eliminating the need for frequent rebalancing operations while maintaining retrieval efficiency.
Solution Approach 2:
The patent uses a nested hierarchical structure where map data is organized into multiple levels of containment: super-blocks containing blocks, and blocks containing sub-blocks. This nested organization allows efficient retrieval by navigating only to the necessary level of detail, reducing the need for complex rebalancing operations across the entire data structure while maintaining O(log n) retrieval complexity.
3Productivity
If contextual information is not considered in map data storage and retrieval, then simpler data structures can be used, but the system cannot filter out irrelevant data, leading to inefficient resource utilization
Solution Approach 1:
The patent applies preliminary action by pre-processing map data during the encoding phase to embed contextual information such as access patterns, spatial relationships, and importance metrics into the hierarchical structure. This preliminary organization allows the retrieval system to automatically filter and prioritize data based on context without requiring complex real-time processing, thus improving resource utilization while controlling complexity.
Data Source
AI summary
An approach is provided for storing and retrieving map data using contextual information priors. The approach involves, for example, processing contextual information to determine a restricted range of location information relevant to at least one query. The approach also involves processing sensor data received from at least one sensor, the sensor data collected at at least one query location, to determine semantic information. The approach further involves filtering the map data based, at least in part, on the restricted range of location information relevant to the at least one query, the semantic information, or a combination thereof.