Distributed Data Processing for IoT Device Performance Analytics
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Solution Overview
Problem
Current approaches to analyzing cognitive products from the Internet of Things (IoT) face challenges in processing and storing vast amounts of data, leading to incomplete analysis and recommendations due to high data acquisition requirements, resulting in inefficient data management and interpretation.
Innovation Solution
A method that retrieves and integrates data from multiple sources to determine recommendations for action, including expected device performance, repair, and replacement frequencies, using a distributed data processing environment that includes a computing device and server interconnected over a network, allowing for autonomous data dissemination and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sources is retrieved and integrated for comprehensive device analysis, then analysis accuracy and recommendation quality improve, but data processing complexity and storage requirements increase
Solution Approach 1:
The system segments data processing by creating separate modules for data retrieval, integration, analysis, and recommendation generation. Each module handles specific aspects of the data workflow, reducing overall system complexity while maintaining comprehensive analysis capabilities across multiple data sources.
Solution Approach 2:
The patent introduces intermediary components including a distributed data processing environment and server infrastructure that mediate between multiple data sources and the analysis engine. These intermediaries standardize data formats and manage integration complexity, allowing accurate multi-source analysis without proportionally increasing processing difficulty.
2Reliability
If vast amounts of data are acquired from IoT devices, then predictive analytics quality improves, but storage requirements and data management overhead increase
Solution Approach 1:
The system extracts only the most relevant and high-value data elements from vast IoT device datasets for storage and detailed analysis. By filtering and selecting critical data points rather than storing all raw data, the system maintains high predictive analytics quality while reducing storage requirements and management overhead.
Solution Approach 2:
The patent implements partial data acquisition and processing, focusing on key performance indicators and critical device parameters rather than comprehensively processing all available data. This selective approach achieves reliable predictive analytics with reduced data storage and management requirements.
Data Source
AI summary
Aspects of the present invention disclose a method, computer program product, and system for determining recommendations for actions based on analysis of a device. The method includes retrieving information associated with a device from one or more databases. The method further includes determining information relevant to device performance as a function of an analysis of the retrieved information associated with the device, where the information relevant to device performance includes one or more factors related to an expected device performance. The method further includes determining a frequency of repair and replacement of one or more components of the device. The method further includes determining a recommendation of an action based on a comparison of an expected frequency of replacement and repair of the components of the device to the determined replacement and repair of the components of the device.