Semantic Time Series Analysis via Relationship Database
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Solution Overview
Problem
Current machine learning systems struggle to effectively recognize subtle relationships between time series data and semantic data, particularly when one indicates trends or anomalies in the other, leading to unrecognizable patterns.
Innovation Solution
A system comprising an analysis component, a prediction component, and a learning component that establishes relationships between semantic data and time series data in a relationship database, generating advisory outputs based on trigger events and determining their accuracy to update the database, using methods like Word2Vec for quantifying semantic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems use traditional methods to analyze time series data, then processing speed is maintained, but the ability to recognize subtle relationships and patterns between semantic data and time series data deteriorates
Solution Approach 1:
The patent introduces an intermediary relationship database that stores pre-established relationships between semantic data elements and time series data elements. This mediator component allows the system to recognize subtle patterns without requiring complex real-time analysis, thereby improving measurement precision while managing system complexity through structured data organization.
Solution Approach 2:
The system performs preliminary actions by pre-processing semantic data and time series data to establish relationships in advance within the relationship database. This preliminary structuring of data relationships enables faster and more accurate pattern recognition during operation, improving measurement precision without proportionally increasing operational complexity.
2Measurement precision
If the system establishes comprehensive relationships between all semantic data and time series data elements, then pattern recognition capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent segments the comprehensive data relationship analysis into manageable components by organizing relationships in a structured database format. Semantic data elements and time series data elements are divided into discrete units with defined relationships, allowing the system to process only relevant segments rather than analyzing all possible combinations, thereby reducing data processing time while maintaining pattern recognition capability.
Solution Approach 2:
The system applies local quality by establishing specific relationship types between different data elements based on their local characteristics and requirements. Rather than applying uniform processing to all data, the system tailors relationship definitions to local data properties, improving pattern recognition efficiency while reducing unnecessary computational overhead.
3Measurement precision
If the system uses detailed semantic data processing methods like Word2Vec, then the accuracy of natural language message recognition is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing semantic data using methods like Word2Vec to convert natural language messages into structured semantic representations before main analysis. This preliminary conversion reduces the computational burden during real-time processing, as the system works with pre-computed semantic vectors rather than raw text, thereby improving recognition accuracy while managing resource requirements.
Solution Approach 2:
The system uses copying by creating semantic representations (vectors) that replicate the meaning of natural language messages in a computationally efficient format. Instead of repeatedly processing raw text during analysis, the system works with copied semantic vectors that preserve meaning while requiring less computational resources, balancing accuracy with resource efficiency.
Data Source
AI summary
Techniques that facilitate semantic and time series analysis using machine learning are provided. In one example, a system includes a data analysis component, a prediction component and a learning component. The data analysis component that establishes one or more relationships between one or more elements of semantic data, including one or more time series identifiers, and one or more elements of time series data in a relationship database. The prediction component generates one or more advisory outputs, wherein generation of the one or more advisory outputs is performed in response to a trigger event. A learning component that determines the one or more relationships in the relationship database, wherein determination of the one or more relationships is based on information indicative of whether the advisory outputs satisfy a defined criterion.