Real-Time Data Science Framework for Operational Insights
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Solution Overview
Problem
Current business intelligence and analytics solutions lack a practical operational approach to data processing and fail to provide precise actions and insights for operational tasks, particularly in real-time data science platforms, relying on simulation-based methods that are not effective with operational data from IoT devices.
Innovation Solution
A framework using AI methods like Machine Learning and Deep Learning to process and analyze real-time data from operational sources, providing insights and recommendations for improving the operation of physical objects by adapting data source designs to reflect their characteristics and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation-based methods are used for data processing, then theoretical models can be built, but they fail to provide precise insights for operational tasks and preventive maintenance
Solution Approach 1:
The patent replaces simulation-based mechanical modeling with data-driven machine learning models that directly process operational data from sensors and APIs. This substitution enables the system to learn actual operational patterns rather than relying on theoretical simulations, providing precise insights for maintenance and operational tasks.
Solution Approach 2:
The system enables physical objects to serve themselves through autonomous monitoring and predictive analytics. By continuously collecting and analyzing operational data, the system can automatically detect anomalies, predict failures, and recommend maintenance actions without human intervention, improving the precision of operational insights.
2Productivity
If real-time data from operational sources is processed, then precise actions and insights can be provided, but the complexity of data processing and model adaptation increases
Solution Approach 1:
The patent segments the data processing framework into distinct modular components: data collection from multiple sources, data preprocessing and cleaning, feature extraction, machine learning model training, and insight generation. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system employs universal machine learning models that can process diverse data types from multiple operational sources (sensors, APIs, logs) through a unified framework. This multi-functionality reduces complexity by avoiding the need for separate processing pipelines for each data source while maintaining the ability to generate precise operational insights.
3Measurement precision
If AI models are trained on operational data to improve precision, then accurate predictions can be made, but the time and resources required for model training and adaptation increase
Solution Approach 1:
The patent implements preliminary actions by pre-processing operational data during collection, cleaning and normalizing data in real-time before it reaches the training pipeline. This preliminary preparation reduces the computational burden during model training, enabling faster iteration while maintaining high prediction accuracy through continuously refined models.
Solution Approach 2:
The system maintains continuous model training and adaptation using streaming operational data, rather than periodic batch training. This continuous learning process allows the models to progressively improve prediction accuracy over time using newly collected data, reducing the total time required to achieve and maintain high precision predictions.
Data Source
AI summary
Systems and methods are described herein for a framework for providing actions and/or insights within a real-time data science platform. First, the system defines a set of data sources to generate a data pipeline, then collects data from the data sources while one or more operations are being performed. The system then configures one or more operational parameters to prepare the data for processing. The system then provides one or more recommended actions and/or insights related to the data based on these operational parameters.