Pivot Table Generation via Cardinality-Based Semantic Ordering
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Solution Overview
Problem
Software applications and services struggle to generate meaningful pivot tables from unstructured data due to their inability to understand the semantic and hierarchical relationships between fields, resulting in sparse and difficult-to-navigate tables.
Innovation Solution
A computer-implemented method and system that automatically generates pivot tables by receiving an indication to generate a pivot table, determining relevant fields, applying cardinality relationship rules to transform fields into a semantic hierarchical order, and populating columns and rows based on cardinality values, ensuring the pivot table is densely populated and easily navigable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If software applications automatically select fields from existing tables for pivot table generation, then the automation process is simplified, but the resulting pivot tables become sparse and difficult to navigate
Solution Approach 1:
The system performs preliminary actions by automatically determining relevant fields, applying cardinality relationship rules to establish semantic hierarchical order, and organizing fields into blocks before pivot table generation. This preliminary structuring ensures that when the pivot table is generated, the fields are already arranged in a logical hierarchy, resulting in dense and navigable tables without requiring manual user intervention.
2Device complexity
If software applications do not intrinsically understand field relationships, then the system complexity is reduced, but the pivot tables become sparse and meaningless
Solution Approach 1:
The system employs self-service mechanisms where the pivot table generation process automatically determines relevant fields, applies cardinality relationship rules to establish semantic hierarchical order, and organizes fields into blocks without requiring external intervention. The system serves itself by intrinsically understanding and organizing field relationships, eliminating the need for manual configuration while preserving semantic meaning.
Solution Approach 2:
The system changes parameters by transforming fields from a simple first order into a semantic hierarchical order based on cardinality relationships. It organizes fields into blocks with specific cardinality values (less than, equal to, or greater than a threshold), which fundamentally alters how the data is structured and presented in the pivot table, converting raw data into semantically meaningful arrangements.
3Loss of time
If fields are arranged in a simple first order, then the processing time is reduced, but the pivot tables lack semantic organization
Solution Approach 1:
The system performs preliminary sorting and organizing actions by applying cardinality relationship rules to fields before pivot table generation. It determines relevant fields, calculates their cardinality values, and arranges them into blocks with specific ordering criteria. This preliminary structuring ensures that when the pivot table is generated, the fields are already organized in a semantic hierarchical order, minimizing additional processing time while achieving stable, meaningful composition.
Data Source
AI summary
In non-limiting examples of the present disclosure, systems, methods and devices for generating pivot tables are provided. A pivot table service may receive an indication to generate a pivot table. The pivot table service may transform a plurality of fields in an existing table in a software application from a first order to a semantic hierarchical order by applying a set of cardinality relationship rules to the plurality of fields. The pivot table service may identify a plurality of blocks comprising one or more fields from the semantic hierarchical order. Fields from each block may be populated and positioned in a pivot table that is being generated based on a cardinality value of each block.