Time Bucketing for Dynamic Association Query Speed
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Solution Overview
Problem
The existing methods for querying dynamic association relationships in big data technologies, such as map reduce, result in low query speed due to high computational burdens.
Innovation Solution
A query method that utilizes time bucketing to store and retrieve dynamic association relationships, allowing for efficient querying by determining the relevant time buckets based on query start and end times, thereby reducing the computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If map reduce method is used to query dynamic association relationships, then comprehensive data analysis can be performed, but query speed becomes slow due to high computational burden
Solution Approach 1:
The patent segments the continuous time range into discrete time buckets, where each bucket stores pre-computed association relationships for a specific time period. This segmentation transforms the original problem of querying all data into querying only relevant time buckets, dramatically reducing computational burden while maintaining comprehensive analysis capability across the entire time range.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating and storing association relationships in time buckets before queries are executed. This preliminary action allows the query system to simply retrieve pre-computed results from relevant time buckets rather than performing complex calculations during query execution, thereby improving query speed while maintaining analysis comprehensiveness.
2Loss of information
If traditional query methods process all time periods, then complete historical analysis is achieved, but calculation burden increases significantly
Solution Approach 1:
By dividing the historical data into segmented time buckets, the system maintains complete historical analysis capability by preserving all time periods in separate buckets, while reducing calculation burden during queries by only processing the specific buckets relevant to the query time range rather than reprocessing all historical data.
Solution Approach 2:
The patent extracts only the necessary time buckets relevant to the query time range from the complete historical data set. This extraction approach maintains historical analysis completeness by selectively accessing relevant periods while eliminating unnecessary computation on unrelated time periods, thereby reducing overall calculation burden.
Data Source
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AI summary
The present application discloses a query method, an apparatus, an electronic device, and a storage medium, and relates to the technical field of spatio-temporal big data in big data technologies. The specific implementation scheme is as follows: receiving a query request sent by a terminal device, where the query request is used to request to query dynamic association relationships of a target entity and includes an identifier of the target entity and query starting and ending time; determining at least one time bucket to be queried in a query database according to the query starting and ending time, where each time bucket stores the dynamic association relationships of the target entity in a time period corresponding to the time bucket; and querying the dynamic association relationships of the target entity in the at least one time bucket according to the identifier of the target entity. Compared with the prior art, the present application uses a time bucketing method to query dynamic association information of the target entity, which allows for effectively reducing the calculation burden and improving the speed for querying the dynamic association information.