Pattern Updating Device for Path Information Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for extracting frequently-occurring patterns from road link sequences require batch processing and are inefficient, especially when new trip data is introduced, as they necessitate re-extraction and aggregation of patterns, leading to increased processing loads.
Innovation Solution
A pattern updating device that includes a processor configured to extract frequently-occurring patterns from path information, store them in a predetermined data structure, and efficiently update these patterns by checking newly acquired path information against the stored data structure, thereby reducing the processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch processing is used to extract frequently-occurring patterns from road link sequences, then pattern extraction can be performed systematically, but the processing load increases significantly when new trip data is introduced, requiring re-extraction and aggregation of patterns
Solution Approach 1:
The system pre-extracts and stores frequently-occurring patterns from historical trip data before new data arrives. When new trip data is introduced, the system uses these pre-stored patterns as a foundation for updating, rather than re-extracting all patterns from scratch. This preliminary action of storing patterns in advance enables efficient incremental updates.
Solution Approach 2:
The system transitions from static batch processing to dynamic incremental processing. Instead of treating pattern extraction as a one-time batch operation, the system makes it dynamic by continuously updating patterns as new trip data arrives. The pattern storage structure is designed to dynamically accommodate new patterns and update existing ones without requiring complete re-extraction.
2Manufacturing precision
If pattern extraction is performed each time new trip data is input, then the patterns reflect the most current data, but the processing time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary extraction and storage of patterns from historical data before new data arrives. When new trip data is introduced, the system uses these pre-stored patterns as a foundation for updating, rather than re-extracting all patterns from scratch. This preliminary action reduces the processing time required for each update.
Solution Approach 2:
The system extracts only the necessary updates to patterns from new trip data, rather than processing all data again. By separating the pattern extraction process into initial batch processing and subsequent incremental updates, the system takes out only the essential updates needed, reducing processing time while maintaining pattern accuracy.
3Ease of manufacture
If simple aggregation methods are used to aggregate extracted patterns by prefix, then the implementation is straightforward, but the number of matching operations becomes proportional to the number of patterns times the pattern length, increasing processing complexity
Solution Approach 1:
The system transforms the pattern storage from a flat structure to a tree-structured data organization. This dimensional change allows patterns to be organized hierarchically by their prefixes, enabling more efficient matching operations. The tree structure reduces the matching complexity from O(number of patterns × pattern length) to a more efficient traversal approach.
Solution Approach 2:
The system segments patterns into hierarchical levels based on their prefixes, creating a structured organization where patterns are divided into groups sharing common prefixes. This segmentation allows the system to process and match patterns more efficiently by traversing the hierarchical structure rather than checking all patterns individually.
Data Source
AI summary
A pattern updating device that includes a memory, and a processor coupled to the memory. The processor is configured to extract frequently-occurring patterns from a plurality of path information, store the extracted patterns as a predetermined data structure, perform a check of newly acquired path information against the stored predetermined data structure, and based on the check, update the frequently-occurring patterns in all path information, including the newly acquired path information, by updating the stored predetermined data structure.