Common-Index Clustering for Sensor and Event/Log Correlation
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Solution Overview
Problem
Interpreting and acting upon large volumes of sensor and event/log data is complex, time-consuming, and prone to human bias, making it challenging to derive valuable insights from these datasets.
Innovation Solution
A data analysis system that preprocesses sensor and event/log data using interpolation and dimensionality reduction, clusters sensor data, and identifies relevant occurrences in event/log data to analyze relationships using a common index, localizing search spaces to detect correlations between the datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of large volumes of sensor and event/log data is performed, then human insight and contextual understanding are obtained, but the process becomes complex, time-consuming, and prone to human bias
Solution Approach 1:
The patent replaces manual mechanical data interpretation with an automated computational system that uses algorithms to process sensor data and event/log data. The system automatically performs clustering on sensor data, identifies occurrences in event/log data, and detects relationships between them, eliminating human bias and significantly reducing analysis time while maintaining or improving accuracy.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw data and human understanding. This system acts as a mediator that transforms voluminous raw data into structured, interpreted insights through automated processing, clustering, and relationship detection, thereby reducing both the time and complexity of manual interpretation.
2Loss of information
If comprehensive analysis of voluminous datasets is performed to detect all relationships, then complete insights are obtained, but the complexity and computational resources required increase significantly
Solution Approach 1:
The patent segments the analysis process into distinct modular components: preprocessing sensor data, clustering sensor data into groups, preprocessing event/log data, identifying occurrences, and detecting relationships between clusters and occurrences. This segmentation reduces overall system complexity by breaking down the comprehensive analysis task into manageable, independent modules that can be processed separately and systematically.
Solution Approach 2:
The patent transforms the complex multi-dimensional relationship detection problem into a more manageable form by using clustering to group sensor data points and then detecting relationships between these clusters and event occurrences. This dimensional transformation simplifies the analysis by working with aggregated cluster representations rather than individual data points, reducing computational complexity while preserving essential relationships.
3Productivity
If automated processing of large datasets is implemented, then analysis speed and objectivity are improved, but the complexity of data preprocessing and processing increases
Solution Approach 1:
The patent applies preliminary action by performing preprocessing operations on both sensor data and event/log data before the main relationship detection process. Sensor data undergoes preprocessing and clustering, while event/log data undergoes preprocessing and occurrence identification. These preliminary steps organize and structure the data in advance, making the subsequent relationship detection more efficient and reducing the complexity of the main processing task.
Data Source
AI summary
A system for analyzing distinct datasets with a common index is configurable to (i) receive input data that includes a first set of data and a second set of data that share a common index; (ii) perform a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints; (iii) identify a set of occurrences within the second set of data (where each occurrence is associated with a respective set of coordinates in the common index), (iv) for each of the set of occurrences: (a) localize search space(s) in the common index using the respective set of coordinates for the occurrence, and (b) facilitate analysis of group(s) of the set of clustered data that are located within the search space(s) to determine whether a relationship exists between the group(s) and the occurrence.