Multi-language Data Mining Model Support via Extension Documents
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Solution Overview
Problem
Data mining models often have hard-coded textual descriptions in a single language, limiting their applicability and usability for users who do not speak that language, particularly in multi-lingual environments where administrators and users need to understand and deploy predictive models across different languages.
Innovation Solution
A computer-implemented method that provides multi-language support by receiving an extension document with textual descriptions in multiple languages, processing requests from front-end applications, and outputting textual descriptions of data mining models and fields in a selected language, using unique identifiers to map language-specific descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If textual descriptions in data mining models are hard-coded in a single language, then the model structure remains simple and easy to maintain, but the usability and applicability are limited for users who do not speak that language
Solution Approach 1:
The patent segments the textual description functionality from the core data mining model by introducing an extension document. The model contains only unique identifiers, while language-specific descriptions are separated into external extension documents. This segmentation allows the model to remain simple while gaining multi-language support through the extension mechanism.
Solution Approach 2:
The patent introduces unique identifiers as intermediaries between the data mining model and textual descriptions. Instead of direct hard-coded descriptions, the model uses identifiers that reference descriptions in extension documents. This intermediary layer enables flexible language support without complicating the model structure, as the identifiers remain constant while descriptions can be translated or localized.
2Ease of operation
If multi-language descriptions are added to data mining models, then usability for international users improves, but the complexity of model deployment and maintenance increases
Solution Approach 1:
The patent extracts textual descriptions from the core data mining model and places them in separate extension documents. This extraction reduces the complexity of the model itself while maintaining ease of operation, as users can access descriptions in their preferred language through the extension documents without affecting the model's core functionality or deployment process.
Solution Approach 2:
The unique identifiers serve a universal function across multiple languages. The same identifier references the same data field or model element regardless of language, allowing the system to maintain universality while supporting multiple languages. This approach simplifies deployment because the identifier mapping remains consistent across different language versions.
3Adaptability or versatility
If textual descriptions are stored as flat strings in PMML, then the format remains simple and compatible, but the ability to support multiple languages is lost
Solution Approach 1:
The patent adds a new dimension to the description storage by introducing language-specific extension documents. Instead of storing descriptions as flat strings within the PMML model, the system creates a separate dimensional layer where descriptions are organized by language. This dimensional change preserves the simplicity of the core model while enabling multi-language support through the extension structure.
Solution Approach 2:
The patent uses copying by referencing the same unique identifier across multiple language versions. Instead of duplicating description data within the model for each language, the system creates copies of the identifier references in extension documents for different languages. This approach preserves language information while maintaining a single source of truth in the model definition.
Data Source
AI summary
An analytical application provider may provide certain middleware functionality that includes updating model output to include textual descriptions of the data mining model and the data fields in a language selected by a front-end application. Certain implementations of the invention relate to a computer-implemented method for providing multi-language support for data mining models. Some implementations relate to computer-implemented method for outputting textual descriptions of data fields in a data mining model in a selected language.