Spatio-Temporal Correlation Detection in Event Sensing
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Solution Overview
Problem
Existing methods struggle to effectively correlate complex spatio-temporal datasets from multiple data sources in real-time, as they are often data-specific and provide sampling-based static solutions, making it challenging for standard techniques to track and validate correlations in events like natural emergencies.
Innovation Solution
A method and system that identify primary and secondary data sources, extract features, categorize them, train classifiers, and detect correlations using a category transfer distribution, enabling the correlation detection among events in a geographical area across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard techniques are used to correlate datasets from multiple data sources, then the implementation is simple, but the ability to effectively correlate complex spatio-temporal datasets in real-time deteriorates
Solution Approach 1:
The patent creates a universal correlation detection system that handles multiple types of spatio-temporal datasets from diverse data sources (social media, sensors, databases) through a unified framework. The system uses generic components including a data receiver, feature extractor, category identifier, and correlation detector that can process any dataset type, making the system multi-functional and adaptable to different data sources while maintaining real-time processing capability
Solution Approach 2:
The patent segments the complex correlation detection task into distinct modular components: data reception, feature extraction, category identification, and correlation detection. Each module performs a specific function and can be independently optimized. This segmentation allows the system to process complex spatio-temporal datasets efficiently by breaking down the overall task into manageable steps that can be executed in real-time
2Ease of manufacture
If data-specific sampling-based static solutions are used, then the implementation is straightforward, but the ability to track and validate correlations in complex events deteriorates
Solution Approach 1:
The patent implements a dynamic correlation detection system that continuously processes incoming datasets and updates correlation relationships in real-time. Unlike static sampling-based solutions, the system adapts to changing data patterns and event developments by continuously extracting features and detecting correlations from new data arrivals, enabling reliable tracking and validation of evolving event correlations
Solution Approach 2:
The patent introduces an intermediary classification layer that maps features from different data sources into a common category space. This intermediary step (category identification) bridges the gap between diverse data-specific formats and the correlation detection mechanism, enabling reliable correlation validation across multiple data sources without requiring data-specific processing for each source
3Loss of information
If massive information from multiple data sources is processed, then the comprehensiveness of event sensing is improved, but the complexity of correlation detection increases
Solution Approach 1:
The patent extracts only the essential features from massive datasets using a feature extraction component that identifies and extracts relevant spatio-temporal characteristics while discarding redundant information. This extraction process reduces the data volume significantly while preserving the critical information needed for correlation detection, thereby managing system complexity without sacrificing information comprehensiveness
Solution Approach 2:
The patent transforms the high-dimensional complex dataset into a lower-dimensional category space through classification. By mapping diverse features from multiple data sources into a unified category dimension, the system simplifies the correlation detection task while maintaining comprehensive event information, effectively reducing system complexity through dimensional transformation
Data Source
AI summary
A method and a system are provided for correlation detection in multiple spatio-temporal datasets for event sensing in a geographical area. The method includes extracting datasets, comprising information about one or more events, from one or more data sources. The method further includes identifying a primary data source and secondary data sources from the one or more data sources. The method further includes extracting primary features from the datasets associated with the primary data source and secondary features from the datasets associated with the secondary data sources. The primary features are categorized into one or more categories. The method further includes training classifiers based on the primary features and/or the one or more categories. The method further includes detecting a correlation among the information associated with the one or more events based on a category transfer distribution from the primary data source to the secondary data sources.