Report Generation Using Reused Statistical Computations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for generating quality reports in enterprises are inefficient due to repetitive statistical computations, leading to excessive computational load on hardware and wastage of user efforts, especially when generating similar reports for different variants or batches of a product.
Innovation Solution
A system and method utilizing a Machine Learning (ML) model and Natural Language Processing (NLP) to identify associations between user inputs and previously performed statistical computations, providing recommendations for report generation, and allowing users to select and modify templates for efficient report creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical computations are performed afresh for each report generation, then the report accuracy and relevance are improved, but the computational load on hardware and time consumption increase significantly
Solution Approach 1:
The system performs statistical computations in advance and stores the results in a database. When a report is needed, the pre-computed results are retrieved and reused instead of recalculating, thereby reducing computational load while maintaining report accuracy.
Solution Approach 2:
The system creates and stores copies of statistical computation results in a database for future reuse. These copied results can be directly utilized in multiple reports without re-computation, reducing the computational burden while preserving data accuracy.
2Measurement precision
If statistical computations are performed for each report, then the report reflects current data accurately, but the time required for report generation increases
Solution Approach 1:
Statistical computations are executed beforehand and their results are stored. This preliminary action eliminates the need for time-consuming recalculations when reports are generated, significantly reducing report generation time while maintaining data accuracy through the use of stored computation results.
Solution Approach 2:
The system maintains a continuous repository of statistical computation results that can be continuously reused across multiple report generations. This eliminates repeated computational cycles and enables continuous, efficient report generation without sacrificing data accuracy.
3Adaptability or versatility
If reports are generated using different formats and configurations by different entities, then the reports meet diverse user requirements, but the complexity of report compilation increases
Solution Approach 1:
The system implements a universal template-based framework that can accommodate diverse user requirements through configurable parameters while maintaining a consistent structural foundation. This allows different entities to generate customized reports without increasing overall compilation complexity, as all reports follow the same underlying template structure.
Solution Approach 2:
The system introduces an intermediary layer of standardized templates and configuration files between the data sources and final report outputs. This intermediary structure mediates between diverse user requirements and the report generation process, simplifying compilation by providing a consistent interface regardless of the specific report format needed.
4Adaptability or versatility
If individuals make independent choices regarding statistical computations and data selection, then the reports are customized to user needs, but the efficiency of the report generation process decreases
Solution Approach 1:
The system enables users to independently configure and customize reports through self-service interfaces where they can select from pre-defined templates and parameters. This allows customization without requiring manual intervention in the computational process, maintaining efficiency while meeting user needs.
Solution Approach 2:
The system allows users to customize reports by changing parameters within a standardized framework rather than making independent choices about the entire computational process. Users can modify specific parameters like date ranges, data sources, or output formats while the underlying computational structure remains efficient and standardized.
Data Source
AI summary
The present disclosure relates to a method for generating reports for a product or process. The method comprises receiving a first user input defining a user role and user requirement for generation of a report of a commercial process or product. The method further comprises identifying one or more data sources storing information related to the commercial process or product, wherein the information includes raw data related to the commercial process or product, processed output obtained by performing statistical computations on the raw data, and reports including the processed output. The method also comprises retrieving the information related to the commercial process or product from the one or more data sources, determining associations of previously performed statistical computations with the first user input and the information related to the commercial process or product. The method further comprises providing one or more recommendations based on the associations, receiving a second user input related to one or more selections made from the one or more recommendation, and generating the report based on the second user input. A system and non-transitory computer-readable storage medium for generating reports are also disclosed.