Temporal Granularity Dataset Segmentation for Query Speed
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Solution Overview
Problem
The increasing volume of data stored by businesses leads to significant delays and increased computing resources required for querying, making it inefficient and frustrating for users to extract insights from large datasets.
Innovation Solution
Generating multiple datasets based on distinct temporal granularities, allowing for the selection and querying of specific datasets that meet temporal data requirements, thereby reducing processing time and improving query efficiency by providing smaller datasets for faster querying and incremental results display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data volume stored by businesses increases, then more information is available for analysis, but query processing time and computing resources increase significantly
Solution Approach 1:
The patent divides the large dataset into multiple smaller datasets based on temporal granularities (e.g., daily, weekly, monthly aggregates). This segmentation allows queries to be executed on smaller, more manageable data subsets, significantly reducing query processing time while preserving access to the full data volume when needed.
Solution Approach 2:
The patent introduces a temporal granularity dimension to organize data. Instead of a single flat dataset, data is structured across multiple temporal levels (hourly, daily, weekly, monthly), enabling queries to leverage appropriate granularity levels and reduce processing time without sacrificing data completeness.
2Quantity of substance
If data volume stored by businesses increases, then more information is available for analysis, but computing resources required for querying increase
Solution Approach 1:
By segmenting data into temporal granularity subsets, the system reduces the computing resources needed for each query. Smaller datasets require less processing power, memory, and computational energy, while the segmented structure allows efficient resource utilization across multiple query operations.
Solution Approach 2:
The patent changes the temporal granularity parameter of datasets to optimize resource usage. By adjusting granularity levels (from fine-grained hourly to coarse-grained monthly), the system can balance between data detail and computational efficiency, reducing energy consumption for querying large volumes of data.
3Speed
If temporal granularity is increased (coarser aggregation), then query speed improves, but data detail and resolution are reduced
Solution Approach 1:
The patent creates multiple segmented datasets at different temporal granularity levels. This allows the system to select the appropriate granularity level based on query requirements - using finer granularity for detailed analysis and coarser granularity for faster queries, thus balancing query speed with data resolution.
Solution Approach 2:
The system dynamically selects the appropriate temporal granularity level based on the specific query requirements. For time-sensitive queries, coarser granularity is used to maximize speed, while for detailed analytical queries, finer granularity is applied to preserve data resolution, making the system adaptive to different operational needs.
Data Source
AI summary
A system and method for displaying data using temporal granularities. The method includes determining at least one first dataset of a plurality of datasets based on at least one temporal data requirement, wherein the plurality of datasets is generated based on a data model, wherein each of the plurality of datasets is generated based further on a distinct temporal granularity of a plurality of temporal granularities, wherein the distinct temporal granularity of each of the at least one first dataset meets at least one of the at least one temporal data requirement; and querying the determined at least one first dataset in order to obtain at least one query result.