Cross-Platform Data Collection Schema for Heterogeneous Sources
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Solution Overview
Problem
Current data storage and management systems face performance issues when handling large volumes of data from diverse sources, requiring efficient cross-platform data collection and processing methods to optimize data handling and storage.
Innovation Solution
A method and system for extracting a cross-platform data-collection schema from heterogeneous interfaces of multiple source platforms, configuring a data-collection schedule, and dynamically modifying it to reflect changes in collected data points and frequencies, enabling ongoing data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data collection frequency and volume are increased to capture more information from multiple platforms, then data completeness and information value are improved, but system performance and computational efficiency deteriorate
Solution Approach 1:
The patent segments data collection by creating a schema that identifies specific collectable data points from different source platforms, allowing the system to collect only relevant data rather than all available data. This segmentation reduces the overall data volume while maintaining completeness of essential information.
Solution Approach 2:
The patent implements partial action by configuring a data collection schedule that collects a subset of available data points at specified frequencies. The system collects only the necessary portion of data required for the specific use case, avoiding excessive data collection that would burden system performance.
2Measurement precision
If data collection schema is made highly specific to capture precise data points, then data relevance and quality are improved, but system complexity and configuration difficulty increase
Solution Approach 1:
The patent creates a universal cross-platform data collection schema that can be applied across multiple source platforms with heterogeneous interfaces. This schema serves multiple functions by standardizing data point identification across different platforms, reducing the need for platform-specific configurations while maintaining data relevance.
Solution Approach 2:
The patent manages complexity by allowing dynamic modification of the data collection schema and schedule. Users can add, remove, or modify data points and collection frequencies without restructuring the entire system, enabling precise data selection while maintaining system simplicity through parameter-level adjustments.
3Adaptability or versatility
If data collection schedule is made dynamic and adaptable to changing requirements, then system flexibility and responsiveness are improved, but configuration management and processing overhead increase
Solution Approach 1:
The patent implements a dynamic data collection schedule that can be modified at runtime based on changing requirements. The system allows users to add, remove, or adjust data points and collection frequencies without requiring system reconfiguration, enabling the schedule to adapt to evolving data needs while maintaining manageable complexity through standardized operations.
Data Source
AI summary
In one embodiment, a method includes extracting a cross-platform data-collection schema based, at least in part, on information available via heterogeneous interfaces of a plurality of source platforms. The cross-platform data-collection schema identifies a plurality of collectable data points in relation to particular source platforms. The method further includes configuring a cross-platform data-collection schedule for the plurality of source platforms. The cross-platform data-collection schedule indicates a collected subset of the plurality of collectable data points in relation to collection frequencies. The method also includes causing data values for the collected subset to be collected on an ongoing basis as dictated by the cross-platform data-collection schedule. Furthermore, the method includes processing a proposed change to at least one of the collected subset and the collection frequencies. In addition, the method includes modifying the cross-platform data-collection schedule to reflect the proposed change.