Spatio-temporal data management system with proximity-based indexing
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Solution Overview
Problem
Current spatio-temporal data management systems face inefficiencies in searching large amounts of data due to large data sizes, lack of consideration for temporal and spatial proximity, continuous data changes, and the need for interpolation, as well as the absence of attribute condition specification and intersection tests, leading to slower search processing.
Innovation Solution
A spatio-temporal data management system that segments data into segments considering temporal and spatial proximity, assigns unique identifiers, and arranges data to optimize storage and retrieval, allowing for efficient search processing by using a processor, memory, and storage device to manage time series data associated with a spatio-temporal region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatio-temporal data is segmented and indexed with traditional methods, then data can be organized for retrieval, but the index data size becomes large and search processing time increases
Solution Approach 1:
The patent segments spatio-temporal data into discrete spatio-temporal segments with unique identifiers, organizing data by time and space dimensions. This segmentation enables efficient indexing by creating compact representations that reduce index data size while maintaining fast search capability through structured organization of segmented data units.
2Loss of time
If spatio-temporal data is stored without considering temporal and spatial proximity, then storage is simpler, but data retrieval time increases due to scattered data locations
Solution Approach 1:
The patent introduces a spatio-temporal dimension to data organization by assigning unique identifiers that encode both spatial and temporal information. This dimensional approach arranges data in storage according to spatio-temporal proximity, enabling efficient retrieval of related data points while maintaining a manageable storage structure through the unified spatio-temporal indexing scheme.
3Reliability
If interpolation processing is performed to handle continuous data changes, then data completeness is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary organization of spatio-temporal data into segmented units with unique identifiers during the data ingestion phase. This preliminary action creates a structured foundation that reduces the need for extensive interpolation processing during queries, as the segmented structure already captures temporal continuity. When interpolation is needed, the pre-organized segments enable faster computation compared to unstructured data.
4Adaptability or versatility
If multiple spatio-temporal data pieces are analyzed for relationships, then comprehensive analysis is improved, but processing complexity and time increase
Solution Approach 1:
The patent merges multiple spatio-temporal data pieces into a unified framework using common spatio-temporal segment identifiers. This merging approach enables relationship analysis across different data sources by establishing connections through shared segment IDs, reducing processing complexity compared to analyzing isolated data pieces. The unified identifier system allows efficient joining and correlation of multiple data streams while maintaining analytical versatility.
Data Source
AI summary
It is provided a spatio-temporal data management system for managing time series data associated with a spatio-temporal region, comprising: a processor for executing a program; a memory for storing the program; and a storage device for storing the time series data. The processor segments the spatio-temporal region in time and space to generate a plurality of spatio-temporal segments, assigns, in consideration of temporal and spatial proximity of the plurality of spatio-temporal segments, identifiers for uniquely identifying the plurality of spatio-temporal segments, each of the identifiers being expressed with a one-dimensional integer value; and determines an arrangement of the time series data so that pieces of data of spatio-temporal segments that are close in the assigned identifiers are arranged close on the storage device.