Unstructured Event Correlation with Time-Series Data
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Solution Overview
Problem
Conventional techniques fail to effectively correlate world events from unstructured data with time-series data to derive cause-and-effect relationships, limiting their ability to provide meaningful insights for enterprise decision-making.
Innovation Solution
A system and method that preprocesses unstructured content to extract world events, aligns and correlates them with time-series data to identify patterns indicative of cause-and-effect relationships, using machine learning to facilitate data analytics such as predicting future events and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional techniques are used to link world events with time-series data, then the process is simple, but the ability to derive cause-and-effect relationships and provide meaningful insights is insufficient
Solution Approach 1:
The patent segments the data processing into distinct modules: unstructured data processing module for extracting world events, time-series data processing module for handling structured data, and correlation module for linking them. This segmentation allows complex data integration to be managed through systematic, isolated processing stages, resolving the contradiction between information quality and processing complexity.
Solution Approach 2:
The patent introduces an intermediary correlation module that acts as a mediator between unstructured world event data and structured time-series data. This intermediary component systematically links the two data types through alignment and correlation processes, enabling cause-and-effect analysis while managing the complexity through a dedicated bridging mechanism.
2Quantity of substance
If unstructured data is processed to extract world events, then the quantity of information is increased, but the processing complexity and time required increase
Solution Approach 1:
The patent applies preliminary action through pre-processing steps including tokenization, stop-word removal, and named entity recognition performed before the main correlation analysis. These preliminary operations prepare and filter the unstructured data in advance, reducing the time required for subsequent processing while maintaining information quantity and quality.
Solution Approach 2:
The patent extracts only the relevant information components from unstructured data through named entity recognition and world event extraction processes. By taking out and isolating only the meaningful elements (entities, events, relationships) rather than processing entire datasets, the system maintains information quantity while significantly reducing processing time and complexity.
3Measurement precision
If machine learning is applied to predict future events, then the accuracy of predictions is improved, but the computational resources and complexity increase
Solution Approach 1:
The patent applies partial action by using machine learning models selectively for prediction tasks rather than applying them universally to all data processing steps. The ML algorithms are deployed only where needed for forecasting future events, maintaining high prediction accuracy while minimizing computational resource consumption compared to applying ML across the entire data pipeline.
Data Source
AI summary
The present subject matter relates to analysis of time-series data based on world events derived from unstructured content. According to one embodiment, a method comprises obtaining event information corresponding to at least one world event from unstructured content obtained from a plurality of data sources. The event information includes at least time of occurrence of the world event, time of termination of the world event, and at least one entity associated with the world event. Further, the method comprises retrieving time-series data pertaining to the entity associated with the world event from a time-series data repository. Based on the event information and the time-series data, the world event is aligned and correlated with at least one time-series event to identify at least one pattern indicative of cause-effect relationship amongst the world event and the time-series event.