Telemetry Data Contextualization Across Disparate Datasets
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Solution Overview
Problem
The challenge lies in effectively correlating and analyzing telemetry data from disparate sources, formats, and devices, which often results in unintelligible data and difficulties in isolating failures or issues within computing systems, IoT devices, and software applications due to the lack of direct key-linked relationships between data entries.
Innovation Solution
The method involves obtaining a data selection of interest, establishing a joined dataset based on contextually corresponding characteristics such as time correlations, and processing it with adaptive functions like machine learning algorithms to correlate data entries, producing contextual correlation datasets that can be evaluated to select relevant output datasets for enhanced error detection and issue isolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If telemetry data is collected from various different sources, formats, and devices, then the quantity and coverage of monitored data is improved, but the data becomes unintelligible and difficult to analyze due to lack of standardization
Solution Approach 1:
The patent applies homogeneity by standardizing disparate telemetry data from multiple sources into a unified format with consistent schemas, data types, and structures. This allows data from heterogeneous sources (different devices, formats, protocols) to be processed uniformly, resolving the contradiction between collecting diverse data and maintaining analyzability.
2Quantity of substance
If telemetry data from multiple sources is accumulated without correlation, then the completeness of data collection is improved, but the ability to isolate failures and issues deteriorates
Solution Approach 1:
The patent merges multiple telemetry data streams into a correlated dataset by establishing relationships between data points from different sources. Through joining operations based on temporal, contextual, and hierarchical relationships, the system combines disparate data while maintaining the ability to trace and isolate specific failures, thus resolving the contradiction between data completeness and failure isolation capability.
3Loss of information
If all telemetry data is processed and stored, then the availability of information is improved, but processor loads and storage requirements increase
Solution Approach 1:
The patent extracts only the relevant and correlated data entries from the full telemetry dataset based on queries, time ranges, and contextual relationships. Instead of processing or storing all raw telemetry data, the system extracts subsets of data that are actually needed for analysis, reducing computational loads and storage requirements while maintaining information availability for meaningful queries.
4Device complexity
If telemetry data lacks contextual relationships, then the simplicity of data collection is maintained, but the intelligibility and usability of data deteriorates
Solution Approach 1:
The patent introduces contextual metadata and relationship descriptors as intermediaries between raw telemetry data and analysis processes. These intermediaries encode temporal relationships, hierarchical structures, and contextual meanings without complicating the data collection process itself, thus maintaining simplicity while enhancing intelligibility.
Data Source
AI summary
Systems, methods, and software for telemetry event correlation is provided herein. An exemplary method includes obtaining an indication of a data selection defining at least one data entry of interest among datasets that comprise data entries determined by one or more associated telemetry elements, establishing a joined dataset based at least on contextually corresponding characteristics derived from the datasets, and processing the joined dataset with a plurality of adaptive functions to correlate data entries of the joined dataset to the data selection, with each of the plurality of adaptive functions configured to produce an associated correlation dataset comprising data entries potentially related to the data selection. The method also includes evaluating each associated correlation dataset to select an output dataset comprising contextual data entries related to the data selection.