Compound Geo-Location Indexing for On-Demand Spatial Queries
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Solution Overview
Problem
Conventional spatial indexes in multi-tenant cloud-based computing environments are not adaptable to on-demand environments, as they cannot easily mix with other data columns, making it difficult to perform queries using spatial information and provide geo-location support for mobile applications.
Innovation Solution
A method is introduced that uses a compound geo-location data type with separate fields for longitude and latitude, employing a distance filtering mechanism to reduce computational complexity by filtering records using regular indexes, and only performing distance calculations for records within a defined radius, thereby enhancing query efficiency in multi-tenant databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional spatial indexes are used in multi-tenant cloud-based environments, then spatial queries can be performed, but they cannot easily mix with other data columns making the system inadaptable to on-demand environments
Solution Approach 1:
The spatial index is segmented into compound data types that separate spatial coordinates (latitude, longitude) from other data columns. This segmentation allows spatial queries to be performed while maintaining the ability to mix with other data types in the same index structure, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The index structure is designed to be universal by supporting both spatial data (through compound geo-location fields) and non-spatial data (through regular columns) within the same indexing mechanism. This multi-functionality enables the system to adapt to on-demand environments while avoiding the need for separate specialized spatial indexes.
2Measurement precision
If distance calculations are performed on all records in the database, then complete geo-location search results are obtained, but computational complexity increases significantly
Solution Approach 1:
The system performs preliminary filtering by creating a bounding box around the target location before executing distance calculations. Records outside this bounding box are excluded in advance, so distance calculations are only performed on a subset of records that are geographically relevant. This preliminary action maintains measurement precision for the final results while dramatically improving productivity by reducing the number of computationally expensive distance calculations needed.
3Device complexity
If regular indexes are used instead of specialized spatial indexes, then the system is simpler and more adaptable, but geo-location queries cannot be efficiently performed
Solution Approach 1:
The patent merges the functionality of regular indexes with spatial query capabilities by using compound data types that include both spatial coordinates and regular data columns in the same index structure. This merging allows the system to maintain simplicity and adaptability of regular indexes while enabling efficient geo-location queries through the integrated spatial components.
Data Source
AI summary
Methods and systems are provided for retrieving, from a database containing a list of records, a subset of the list of records located within a user defined distance from a target point, each record in the list of records having a compound geo-location data type including a first data field and a second data field. The method involves generating a circle around the target point; identifying records having a geo-location within the circle; including the identified records in a result set; and presenting the result set to a user on a display screen. The method further includes treating the first data field and the second data field as a single data element.