ML Analytics Platform Ontology Mapping for Predictive Modeling
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Solution Overview
Problem
Existing data analytics platforms face challenges in bridging the gap between problem definition and data provisioning, are costly and time-consuming, lack data governance, and struggle with managing product variants and emerging devices and services, making them inefficient and unreliable for deriving insights from diverse data sources.
Innovation Solution
A machine learning-based analytics platform with a graphical user interface and central data analysis module that enables stakeholders to define business problems, configure data sources, translate and visualize data, deploy models in distributed environments, and monitor and re-train models for predictive analysis, using ontology mapping and distributed storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manual coding methods are used to solve business problems, then customization capability is improved, but development time and cost increase significantly
Solution Approach 1:
The patent uses template-based model copying where pre-defined analytical models can be replicated and reused across different business problems. Instead of manually coding each solution from scratch, users can copy existing models and customize them, dramatically reducing development time while maintaining adaptability to different business scenarios.
Solution Approach 2:
The platform implements universal model templates that can serve multiple business problems. A single analytical model template can be applied to various business scenarios with different parameters and configurations, eliminating the need for separate manual coding for each problem and reducing overall development time.
2Measurement precision
If extensive manual coding is performed for each business problem, then problem-specific accuracy is improved, but productivity decreases
Solution Approach 1:
By copying and reusing validated model templates, the system maintains high problem-specific accuracy while avoiding redundant coding work. The templates encapsulate proven analytical logic that can be accurately applied to new problems through configuration rather than rewriting, thus preserving accuracy while boosting productivity.
Solution Approach 2:
The patent implements pre-defined model templates that contain pre-coded analytical logic and algorithms. This preliminary action of creating reusable templates in advance allows users to quickly deploy accurate solutions without performing extensive manual coding for each new business problem, thereby maintaining accuracy while improving productivity.
3Loss of energy
If existing infrastructure investments are relied upon, then cost efficiency is improved, but adaptability to emerging devices and services deteriorates
Solution Approach 1:
The platform uses universal data models and ontologies that can represent both existing and emerging devices and services. This universal framework allows the system to work with current infrastructure investments while simultaneously adapting to new devices and services without requiring complete system redesign, thus maintaining cost efficiency while improving adaptability.
Solution Approach 2:
The patent implements dynamic model templates that can be configured and updated to accommodate emerging devices and services. The system allows runtime adaptation of model parameters and data sources, enabling infrastructure to evolve with new technologies while leveraging existing investments, thereby balancing cost efficiency with adaptability.
4Quantity of substance
If data from multiple sources is integrated, then comprehensiveness of analysis is improved, but data governance complexity increases
Solution Approach 1:
The patent introduces an ontology layer as an intermediary between diverse data sources and analytical models. This ontology acts as a standardized mediation framework that translates various data formats and structures into a common representation, enabling comprehensive data integration while simplifying governance by providing a unified data model that masks the underlying complexity of multiple sources.
Data Source
AI summary
The present subject matter discloses a system and method to enable a machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stakeholders to generate and manage a model for predictive analysis. The method may further comprise enabling a business user to define the business problem, and generate models to perform predictive analysis. The method may further comprise deploying the model, in a distributed environment, over a target platform. The method may further comprise monitoring the model to identify at least one error in the model and re-training the model for performing predictive analysis based on the at least one error, thereby enabling the machine learning based analytics platform.