Generational Database Tables for Time Series Data Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional approaches to managing time series data in database tables are inefficient, leading to high operational and resource costs due to the need for full access parameters for growing data volumes, resulting in unnecessary resource usage and potential data loss in case of hardware or network failures.
Innovation Solution
A method and system that generate and manage generational data tables, where each table maintains a subset of time series data based on timing information, with initial write and read access parameters, and dynamically adjust access parameters as needed, optimizing resource usage and reducing costs by limiting access to only required portions of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional database tables are used to store growing time series data, then data storage capacity increases, but operational and resource costs increase due to the need for full access parameters
Solution Approach 1:
The patent divides a single large database table into multiple generational data tables (e.g., current generation table for recent data, archived generation tables for historical data). Each generational table stores a specific time-range subset of the time series data. This segmentation allows the system to apply different access parameters to different data generations, reducing the need for full access parameters across the entire dataset and thereby lowering operational and resource costs.
Solution Approach 2:
The patent implements local quality by assigning different access parameters to different generational data tables based on their age and importance. Current generation tables maintain full read-write access parameters for active data, while archived generation tables use restricted read-only or limited write access parameters. This localized differentiation of access parameters optimizes resource usage by applying appropriate access control only where needed, rather than uniformly across all data.
2Ease of operation
If full access parameters are maintained for all data in database tables, then data accessibility is improved, but resource usage increases unnecessarily
Solution Approach 1:
The patent segments data into generational tables with different access parameter sets. Current generation tables maintain full access parameters ensuring high data accessibility for recent data, while archived generation tables use restricted access parameters. This segmentation ensures that full resource usage is only applied to data that actually requires it, improving the ratio of accessibility to resource consumption.
Solution Approach 2:
The patent implements dynamic access parameter adjustment where access parameters are not static but change over time as data transitions between generations. When data moves from current to archived generation, its access parameters are dynamically adjusted from full read-write to restricted access. This dynamic approach ensures optimal resource usage while maintaining necessary accessibility for different data lifecycles.
3Device complexity
If traditional database approaches are used, then data storage is simplified, but data loss risk increases in case of hardware or network failures
Solution Approach 1:
The patent divides data into multiple generational tables with different retention policies and access parameters. Current generation tables store recently written data with full access parameters, while archived generation tables store historical data with restricted parameters. This segmentation creates natural redundancy and isolation boundaries that protect against data loss - if one generation table is compromised, other generations remain intact, reducing overall data loss risk while maintaining storage organization.
Solution Approach 2:
The patent implements beforehand cushioning by maintaining multiple generational copies of data with different access parameters and retention periods. Archived generation tables serve as protective copies that can recover data if current generation tables are lost or corrupted. This prior preparation of alternative data storage paths cushions against hardware or network failures, reducing data loss risk before failures occur.
Data Source
AI summary
A database service that receives and manages time series data into a set of generational data tables. The database service generates a set of generational data tables configured to receive a subset of time series data. Each generational data table is created with an initial set of access parameters that facilitates the receipt of incoming time series data. Thereafter, the database service creates additional generational data tables to receive the continuous received time series data and then modifies the access parameters of the previously created data table, such as for read only rights.