Geospatial Data Preloading for Fast Response and Data Freshness
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Solution Overview
Problem
Existing geospatial data systems experience long response times due to the need for significant data acquisition and processing, requiring large storage and computational resources, and struggle with frequent data updates from unexpected events.
Innovation Solution
A geospatial data platform that acquires and prepares geospatial data in advance based on predefined acquisition conditions, including region and time criteria, to provide data to applications promptly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geospatial data is acquired and processed in real-time in response to user requests, then data freshness and accuracy are improved, but response time increases significantly
Solution Approach 1:
The system performs preliminary actions by acquiring and processing geospatial data in advance based on predicted future requests, rather than waiting for actual user requests. This allows the data to be ready before needed, resolving the contradiction between data freshness and response time.
Solution Approach 2:
The system dynamically adjusts data acquisition strategies based on predicted request patterns and changing conditions. By using machine learning to forecast future requests and adaptively selecting what data to prepare in advance, the system optimizes both responsiveness and data freshness.
2Adaptability or versatility
If a large amount of geospatial data is downloaded and processed in advance to meet various service demands, then service adaptability is improved, but storage and computational resources increase significantly
Solution Approach 1:
The system performs preliminary data acquisition and processing based on predicted future requests rather than preparing all possible data in advance. This selective preliminary action maintains service adaptability while reducing the quantity of stored data.
Solution Approach 2:
The system applies partial action by acquiring and processing only the necessary subset of geospatial data predicted to be needed in the future, rather than preparing all possible data. This approach maintains sufficient service adaptability while minimizing storage and computational resource consumption.
3Speed
If general data cache technology is applied to handle frequent data changes, then data update speed is improved, but the system cannot accurately cope with unexpected timing changes and new data registrations
Solution Approach 1:
The system uses feedback mechanisms where machine learning models continuously learn from actual user behavior patterns and data change events. This feedback loop enables the system to accurately predict future data changes and adjust data acquisition strategies accordingly, improving both update speed and accuracy.
Solution Approach 2:
The system performs self-service by automatically learning from observed data patterns and adjusting its own data acquisition and processing strategies without external intervention. This self-adaptive capability allows the system to cope with unexpected data changes and new registrations more accurately.
Data Source
AI summary
A geospatial data platform is a system that acquires geospatial data corresponding to an acquisition condition registered in advance and provides the acquired geospatial data to the application. The acquisition condition includes region conditions related to a geographical range of the geospatial data in future and time conditions related to an acquisition timing of the geospatial data. For each acquisition timing designated by the time conditions, the geospatial data satisfying the region conditions at the acquisition timing is prepared before a request is made from the application.