Meta-labeling for Data Change Event Detection
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Solution Overview
Problem
Current methods for detecting anomalies and events of interest in large datasets are impractical and inefficient, relying on manual processes that are time-consuming and prone to missing critical changes, which can lead to security or access threats.
Innovation Solution
A system that crawls data and metadata to create complex meta-labels, analyzing both current and past states of the data to identify anomalies and automatically notify relevant parties, thereby reducing the time-to-discovery of business-critical insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual detection methods are used to identify events of interest in datasets, then human judgment and contextual understanding can be applied, but the process is time-consuming and impractical for large datasets
Solution Approach 1:
The patent replaces manual human detection processes with an automated computer-based system that uses algorithms to identify events of interest in datasets, eliminating the need for manual scanning while maintaining detection accuracy
Solution Approach 2:
The system automatically monitors datasets, detects changes, and generates notifications without requiring continuous human intervention, enabling the system to self-monitor and alert users to events of interest autonomously
2Productivity
If automated detection systems are implemented to monitor data changes, then detection speed increases, but the complexity of defining and learning what constitutes data of interest increases
Solution Approach 1:
The system performs preliminary actions by pre-defining event criteria and monitoring parameters before actual detection occurs, allowing the automated system to efficiently identify events of interest without complex real-time decision-making
Solution Approach 2:
The system incorporates feedback mechanisms that allow it to learn from detected events and adjust its monitoring parameters, improving its ability to identify relevant events while reducing the need for manual reconfiguration
3Ease of manufacture
If traditional file change audit trails are used to monitor data, then implementation is straightforward, but the ability to capture all events of interest and predict trends is insufficient
Solution Approach 1:
The patent segments the monitoring process into multiple layers: traditional file change tracking, data content analysis, and trend prediction, allowing each component to function independently while collectively providing comprehensive event detection capability
Solution Approach 2:
The system adds new dimensions to traditional audit trails by incorporating temporal patterns, data content analysis, and predictive modeling, transforming static change records into dynamic, multi-dimensional event intelligence
4Measurement precision
If manual scanning of large datasets is performed to detect critical changes, then comprehensive coverage can be achieved, but the time lag allows problems to grow unchecked
Solution Approach 1:
The system continuously monitors datasets in real-time, maintaining constant surveillance to detect changes immediately as they occur, eliminating the intermittent nature of manual scanning and preventing time lags that allow problems to escalate
Data Source
AI summary
One example method includes crawling data included in a dataset, based on the crawling of the data, creating and/or obtaining metadata concerning the data, crawling the metadata and obtaining information about a data change event involving the data, based on the crawling of the metadata, creating a meta label that documents occurrence of the data change event, and associating the meta label with the metadata. An analysis of the data, metadata, and meta label, may be performed and a trigger generated based on the analysis.