Space-Time Context Query Optimization via Space-Filling Curve Mapping
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Solution Overview
Problem
IoT systems face challenges in efficiently managing and querying massive amounts of space-time context data due to the need for fast search and indexing across variable spatial scales, particularly when dealing with stationary and moving objects, which results in a high number of queries and suboptimal scale levels.
Innovation Solution
A method and system that map query ranges in a multi-dimensional space into fragments of a space-filling curve, using each fragment as a key to query the database for space-time context objects, optimizing the number of fragments by combining intersecting or neighboring fragments, thereby reducing the number of query operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional indexing methods are used for space-time context data, then data can be stored in the database, but the search and query operations become inefficient when dealing with massive amounts of data across variable spatial scales
Solution Approach 1:
The query range in multi-dimensional space is segmented into multiple fragments along the space-filling curve. Each fragment is processed as a separate query operation, allowing the system to efficiently retrieve context objects by dividing the large search space into manageable segments that can be queried in parallel or sequence.
Solution Approach 2:
The patent transforms multi-dimensional spatial queries into one-dimensional queries along the space-filling curve (e.g., Hilbert curve). By mapping N-dimensional space onto a 1D curve, the system simplifies the indexing and query operations while preserving spatial locality, enabling efficient range queries across variable spatial scales.
2Reliability
If the query range is mapped into many fragments along the space-filling curve, then all context objects within the range can be found, but the number of query operations increases
Solution Approach 1:
Adjacent or overlapping fragments along the space-filling curve are merged into larger query ranges. This reduces the total number of separate query operations while ensuring that all context objects within the original query range are still retrieved, as the merged fragments collectively cover the same spatial-temporal region.
Solution Approach 2:
The system dynamically adjusts the fragmentation level and merging strategy based on the query characteristics and data distribution. By adaptively controlling the number and size of fragments, the system optimizes the balance between query completeness and the number of operations required.
Data Source
AI summary
A method and/or system for managing a database that stores space-time context objects is provided. The system receives a query range in a multi-dimensional space. The system maps the query range into a set of fragments of a space-filling curve that fills the multi-dimensional space in all dimensions of the multi-dimensional space. The system uses each mapped fragment in the set of mapped fragments as a key to query the database for space-time context objects that are mapped to the space-filling curve. The system queries the database by identifying one or more context objects that intersect the mapped fragment at the space-filling curve.