Data Cell to Model Object Transformation for Interoperability
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Solution Overview
Problem
Current modeling platforms, whether mainframe-based or entry-level spreadsheet/database applications, face limitations in handling large-scale data sets and generating predictive models, with proprietary data structures and lack of interoperability, making it difficult to extract and export data cells as separate units for advanced modeling operations.
Innovation Solution
The development of systems and methods that allow data cells to be transformed into model objects in a native object-based or object-compatible format, enabling seamless extraction, generation, and communication of modeling objects between various platforms, including mainframe systems, data centers, and other resources, using a modeling client with an API for data manipulation and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If mainframe-based modeling software is installed to handle sophisticated modeling applications, then modeling capability and data processing power are improved, but system cost and operational complexity increase significantly
Solution Approach 1:
The system separates the modeling application interface from the computational engine. The desktop application handles user interaction and data preparation, while the mainframe provides backend computational power. This segmentation allows users to access sophisticated modeling capabilities without directly managing complex mainframe systems.
Solution Approach 2:
The patent introduces an intermediary desktop application that mediates between the user and the mainframe system. This intermediary handles data extraction, transformation, and communication with the mainframe, shielding users from the complexity of mainframe operations while providing access to advanced modeling capabilities.
2Ease of operation
If spreadsheet or database applications are used for data entry and reporting, then ease of operation is improved, but ability to generate predictive models and handle large data sets deteriorates
Solution Approach 1:
The system merges the familiar spreadsheet interface with advanced modeling capabilities. Users can continue to enter and manipulate data in a spreadsheet-like environment while the system automatically extracts relevant data, transforms it into appropriate formats, and sends it to the mainframe for sophisticated modeling and predictive analysis.
Solution Approach 2:
The desktop application serves as an intermediary that bridges spreadsheet applications and mainframe modeling systems. It extracts data from spreadsheets, transforms it into suitable formats, and communicates with the mainframe, enabling users to leverage both the ease of spreadsheet operations and the power of mainframe modeling without requiring separate systems.
3Adaptability or versatility
If proprietary data structures are used in spreadsheet and database applications, then application-specific functionality is improved, but interoperability and data extraction capability deteriorate
Solution Approach 1:
The system extracts data from proprietary spreadsheet and database structures by identifying and pulling out relevant data elements, their relationships, and associated metadata. This extraction process converts proprietary formats into a standardized representation that can be processed by the modeling system while preserving the original data's meaning and context.
Solution Approach 2:
The patent transforms data parameters from proprietary formats to standardized formats. The system changes the representation of data elements, their relationships, and structural parameters into a universal format that maintains the semantic meaning while enabling interoperability with the mainframe modeling system.
Data Source
AI summary
Embodiments relate to systems and methods for extracting a data cell transformable to a model object. Aspects relate to object-based modeling using modeling objects that can be extracted from spreadsheet cells, database entries, or other sources. A modeling client can host modeling logic and an application programming interface (API) to create, access, manipulate, and import/export modeling objects used in modeling applications, such as engineering, medical, financial, and other modeling platforms. In aspects, the source data can be accepted into the modeling client from consumer or business-level applications, whose cell, database, or other data content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. Modeling operations can therefore be performed on or incorporate data that was not originally strictly configured for object-based modeling applications. The extracted model object can also be exchanged with other applications or platforms.