Data Science Platform for Predictive Insight Integration
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Solution Overview
Problem
Organizations face challenges in effectively processing and analyzing large volumes of diverse data from various sources, such as machine sensors and IoT devices, due to the requirement for specialized skills and custom programming, which limits the integration of predictive insights into business applications.
Innovation Solution
A data science platform (DSP) is integrated with a data management solution to orchestrate data pipelines and provide predictive capabilities, allowing users to create and consume predictive models without needing expertise in machine learning, through a user interface that enables the selection and execution of machine learning operators and visualization operators, and exposes insights via REST/OData services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized skills and custom programming are required for data analysis, then measurement precision and manufacturing precision are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces a data management platform as an intermediary layer between raw data sources and business applications. This platform contains pre-built machine learning models and algorithms that automatically process and analyze data, eliminating the need for users to write custom programming code while maintaining high analysis precision. The platform acts as a mediator that translates complex data processing tasks into automated, accessible services.
Solution Approach 2:
The system enables self-service data analysis by providing automated machine learning models that can be directly applied to data without requiring specialized programming skills. Users can upload their data and receive analyzed insights through the platform's automated processing pipelines, which self-manage the complex tasks of data cleaning, feature engineering, and model application.
2Measurement precision
If specialized skills are required for predictive analytics, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The data management platform serves as an intermediary that encapsulates complex machine learning expertise within standardized, easy-to-use services. Business users can access predictive analytics capabilities through simple interfaces without needing to understand the underlying complex algorithms, while the platform maintains high predictive accuracy through its sophisticated model library.
Solution Approach 2:
The patent creates a universal platform that provides multiple machine learning algorithms and analytical capabilities through a single integrated system. This multi-functional platform serves diverse analytical needs across different business applications while maintaining consistent ease of use, allowing the same interface to support various types of predictive analytics without requiring users to learn different tools or skills.
3Manufacturing precision
If custom programming is required for data processing, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The patent employs pre-built, reusable machine learning models and data processing templates that can be copied and applied to different datasets and business scenarios. Instead of requiring custom programming for each project, the system provides standardized analytical components that maintain high processing accuracy while dramatically reducing implementation effort. These copied models can be adapted to different use cases through configuration rather than code writing.
Solution Approach 2:
The system performs preliminary actions by pre-processing data and pre-training machine learning models before they are needed for specific business applications. Complex data cleaning, feature extraction, and model training are completed in advance by the platform, so when users need analytics, the heavy lifting has already been done. This preliminary preparation maintains processing accuracy while making the actual user interaction simple and fast.
Data Source
AI summary
Techniques are described for integrating prediction capabilities from data management platforms into applications. Implementations employ a data science platform (DSP) that operates in conjunction with a data management solution (e.g., a data hub). The DSP can be used to orchestrate data pipelines using various machine learning (ML) algorithms and/or data preparation functions. The data hub can also provide various orchestration and data pipelining capabilities to receive and handle data from various types of data sources, such as databases, data warehouses, other data storage solutions, internet-of-things (IoT) platforms, social networks, and/or other data sources. In some examples, users such as data engineers and/or others may use the implementations described herein to handle the orchestration of data into a data management platform.