Dynamic Interval Normalization in Cloud Data Warehouses
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Solution Overview
Problem
Users face challenges in fully utilizing data stored in cloud-based data warehouses due to the complexity of manipulating data, which often requires constructing intricate queries beyond the capabilities of most users.
Innovation Solution
A method for dynamically normalizing intervals in a table, involving a query manager system that receives requests from a client computing system, generates an interval table based on a data range, joins it with the data set, and presents the result as a worksheet through a graphical user interface, enabling users to visualize and interact with the data more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users directly manipulate data in cloud-based data warehouses using database query languages, then data processing capability is maintained, but user accessibility and ease of operation deteriorate due to complex query construction requirements
Solution Approach 1:
The patent introduces a data normalization service as an intermediary layer between users and the cloud-based data warehouse. This service automatically generates and executes complex SQL queries, interval table creations, and data joining operations based on simple user inputs (data set reference and data range), thereby shielding users from query construction complexity while maintaining full data processing capability
Solution Approach 2:
The system implements self-service through automated interval table generation and data normalization. The service automatically creates interval tables based on specified data ranges, performs joins between interval tables and data sets, and presents results in normalized formats without requiring user intervention in the complex manipulation steps
2Productivity
If complex queries are used to manipulate data in the database, then data manipulation capability is maintained, but productivity deteriorates due to time-consuming query construction and execution
Solution Approach 1:
The system performs preliminary actions by pre-generating interval tables based on specified data ranges before actual data analysis operations. These pre-computed interval structures are then efficiently joined with data sets, eliminating the need for users to construct complex queries for each analysis operation and significantly reducing query construction time
Solution Approach 2:
The patent transforms the parameter representation by converting raw data ranges into structured interval tables with defined boundaries and relationships. This parameter transformation enables efficient data normalization and joining operations, improving productivity by replacing complex ad-hoc query construction with parameter-based interval operations
Data Source
AI summary
Dynamically normalizing intervals in a table including receiving, from a client computing system, a request to normalize intervals for a data set on a cloud-based data warehouse, wherein the request comprises a reference to the data set and a data range; generating, on the cloud-based data warehouse, an interval table using the data range; joining, into a joined table on the cloud-based data warehouse, the interval table and the data set; receiving the joined table from the cloud-based data warehouse; and presenting, via a graphical user interface on the client computing system, the joined table as a worksheet.