IoT Data Rollup Engine for Sensor Aggregation
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Solution Overview
Problem
Raw sensor data from machine and equipment assets is not user-friendly and requires conversion into a more usable format for monitoring and analytics, especially when data comes from multiple sources with different characteristics.
Innovation Solution
A data rollup engine processes sensor data to generate rolled-up data with user-determined granularity, enabling it to be used across different machines and systems, and executes operations to generate information associated with IoT assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If raw sensor data is collected from multiple IoT assets, then data completeness and coverage are improved, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex data processing task into distinct functional components: a rollup engine that aggregates data from multiple sources, operation definitions that specify processing logic, and result generators that produce standardized outputs. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining data completeness from multiple IoT assets.
2Measurement precision
If raw sensor data is processed without aggregation, then data detail and precision are maintained, but data usability and interpretability decrease
Solution Approach 1:
The patent implements dynamic aggregation through configurable operation definitions that allow users to specify rollup functions, time windows, and grouping criteria. The system can dynamically adjust the level of aggregation based on user needs, maintaining detailed data when precision is required while providing aggregated summaries when usability is prioritized. This dynamic approach resolves the contradiction by allowing the same data to serve both detailed analysis and high-level monitoring purposes.
3Manufacturing precision
If data is aggregated with high granularity, then data detail and analytical capability are improved, but data volume and processing load increase
Solution Approach 1:
The patent applies partial aggregation by allowing users to specify which operations and data points should be aggregated versus which should remain at full detail. The operation definitions enable selective rollup where only certain metrics are aggregated while others maintain their original granularity. This partial action approach provides the necessary analytical capability at appropriate granularities without unnecessarily increasing processing load across all data points.
4Adaptability or versatility
If custom data processing operations are implemented, then analytical capability and business value are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a universal data processing framework where operation definitions serve multiple functions: they define aggregation logic, specify time windows, determine grouping criteria, and control output formats. This multi-functional operation definition system allows the same mechanism to handle various analytical requirements across different IoT assets and use cases, reducing the need for separate custom implementations and thereby reducing overall system complexity while maintaining high adaptability.
Data Source
AI summary
The example embodiments are directed to a data rollup engine for use in software applications hosted by a cloud platform such as in an Internet of Things. As one example, the method includes receiving sensor data associated with at least one Internet of Things (IoT) asset, processing a data rollup engine on the sensor data to generate rolled-up sensor data having a granularity determined by a user, and executing one or more operations on the rolled-up sensor data to generate information associated with the at least one IoT asset. The rollup engine is not limited to a particular type of asset and enables substantial amounts of raw time series data to be rolled up and grouped based on properties thereof.