Automated Cross-Tabular Report Generation via Field Scoring
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Solution Overview
Problem
Current spreadsheet application programs require two learning curves for users to understand and utilize cross-tabular report features, leading to a poor user experience, especially for novice users.
Innovation Solution
A computer system automatically determines the suitable placement of fields in a cross-tabular report by scoring fields based on information types and selecting the highest-scoring fields for row, column, or value placement, thereby simplifying the creation of cross-tabular reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current application programs provide cross-tabular report features, then data summarization capability is improved, but user experience deteriorates due to two learning curves
Solution Approach 1:
The system performs automatic field scoring and placement determination without requiring user intervention. The computer autonomously analyzes source data fields, scores them based on information types, and determines optimal placement in the cross-tabular report, eliminating the need for users to learn manual configuration procedures.
Solution Approach 2:
The system pre-processes source data by determining information types for each field and calculating suitability scores before the user creates the report. This preliminary analysis of field characteristics and automatic scoring prepares the data structure in advance, so when the user initiates report creation, the system can immediately generate an optimized layout without requiring user learning or manual setup.
2Ease of operation
If automatic field scoring and selection is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The complex task of cross-tabular report generation is segmented into distinct processing stages: determining information types for each field, scoring fields based on those types, selecting fields with highest scores, and placing them in appropriate positions. This segmentation of the automated process manages complexity by breaking it into discrete, manageable steps that can be executed sequentially.
Solution Approach 2:
The system transforms unstructured source data into a scored and ranked format by applying information type classification and numerical scoring parameters. Each field is assigned scores based on its information type characteristics, converting qualitative data properties into quantitative metrics that enable automatic selection and placement decisions.
Data Source
AI summary
Cross-tabular reports may be automatically created by a computer from received source data. After receiving the source data, the computer may determine different information types associated with fields contained in the source data. The computer may then score each field based on the information types. A score describes a suitable placement of each field as a cross-tabular report row, a cross-tabular report column or a cross-tabular report value field. The computer may then select the fields having the highest score for placement as cross-tabular report rows, cross-tabular report columns or cross-tabular report value fields in a cross-tabular report. Finally, the computer may build the cross-tabular report with the selected fields.