Metadata Correlation Engine for IoT Data Retrieval
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Solution Overview
Problem
The proliferation of cloud services and IoT devices leads to data fragmentation, making it difficult for users to quickly retrieve and access data across multiple platforms, resulting in time loss and increased probability of data not being found timely.
Innovation Solution
A system comprising a scanning engine, storage, analysis engine, search engine, security exchange, and display engine that performs data reticulation by accessing and correlating metadata from different locations, generating new metadata, and enabling secure, formatted data retrieval and transfer across various cloud services and IoT platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored across multiple cloud services and IoT platforms, then data volume and service capability increase, but data accessibility and retrieval time worsen
Solution Approach 1:
The system performs preliminary actions by proactively scanning and collecting metadata from multiple cloud services and IoT platforms before data is needed. The scanning engine continuously gathers metadata information and stores it in a centralized metadata repository, so when a user searches for data, the system can quickly query pre-collected metadata instead of searching across all distributed platforms in real-time.
Solution Approach 2:
The patent introduces a metadata repository and analysis engine as intermediary components between users and distributed data sources. The analysis engine processes metadata to identify correlations and relationships, creating an indexed knowledge structure that mediates between the user's search query and the scattered data across multiple platforms, significantly reducing retrieval time.
2Adaptability or versatility
If data is distributed across multiple locations, then service versatility increases, but data correlation and analysis capability worsen
Solution Approach 1:
The system extracts metadata from distributed data sources and separates it from the actual data storage locations. By taking out only the essential descriptive information (metadata) and concentrating it in a centralized repository, the system enables comprehensive data correlation without requiring physical consolidation of all data. The analysis engine then processes this extracted metadata to identify relationships and correlations that would be difficult to detect across scattered sources.
Solution Approach 2:
The metadata repository serves multiple functions: it stores metadata from various cloud services and IoT platforms, enables correlation analysis, supports search operations, and provides a unified interface for data discovery. This universal component handles diverse data types and sources through a common metadata schema, maintaining service versatility while enabling centralized analysis.
3Reliability
If comprehensive data scanning is performed across all platforms, then data completeness improves, but system complexity and processing overhead worsen
Solution Approach 1:
The system segments the data collection and processing functions into distinct modular components: a scanning engine that collects metadata, a metadata repository that stores it, and an analysis engine that processes it. This segmentation allows each component to be optimized independently and enables parallel processing of metadata from different data sources, improving data completeness while managing system complexity through modular architecture.
Data Source
AI summary
A system designed with a scanning engine, storage, analysis engine, search engine, security exchange, and display engine. The system performs data reticulation using the scanning engine to access a first piece of data stored at a first location and a second piece of data stored at a second location. The scanning engine further retrieves from the first and second pieces of data first and second metadata, related respectively. The analysis engine creates the correlated metadata based on the first and second metadata. An example of the correlated metadata contains information not present in the first metadata and the second metadata.