Multidimensional Timeseries Model Generation For Monitoring Data
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Solution Overview
Problem
Current monitoring systems face challenges in integrating and analyzing monitoring data from diverse, potentially unrelated sources, leading to fragmented views and laborious manual processes for holistic analysis.
Innovation Solution
A system that ingests monitoring data from various sources and transforms it into a unified, multidimensional timeseries data format, enabling the creation of demand-specific models for analysis and visualization of monitoring artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If monitoring data from multiple diverse sources is collected to improve observability, then the quantity and diversity of monitoring data increases, but the complexity of integrating and analyzing the data increases
Solution Approach 1:
The patent segments monitoring data into distinct dimensional categories (time, resource, service, application, host, container, process, etc.) and organizes them in a structured timeseries format. This segmentation allows diverse monitoring data from multiple sources to be systematically categorized and integrated without overwhelming complexity, as each data point is assigned to specific dimensions that facilitate organized analysis.
Solution Approach 2:
The patent introduces a multidimensional framework where monitoring data is organized along multiple axes (time series, resource dimensions, service dimensions, application dimensions, host dimensions, container dimensions, process dimensions). This dimensional transformation converts unstructured diverse data into a structured multidimensional space, enabling efficient integration and analysis by providing a common organizational structure across different data sources.
2Ease of manufacture
If manual processes are used to integrate monitoring data from different sources, then data integration can be performed, but the time and effort required for analysis increases
Solution Approach 1:
The patent performs preliminary organization of monitoring data into a standardized multidimensional timeseries structure during data ingestion. By pre-categorizing data along multiple dimensions and storing it in a unified format, the system eliminates the need for manual integration efforts during analysis phases, enabling rapid querying and cross-correlation of data from different sources without time-consuming manual processing.
Solution Approach 2:
The patent introduces a unified multidimensional data model as an intermediary layer between diverse monitoring data sources and analysis processes. This intermediate representation standardizes different data formats and structures into a common framework, facilitating automated integration and analysis while eliminating manual intervention requirements.
3Reliability
If specialized monitoring solutions are used for different service technologies, then monitoring coverage for specific technologies improves, but unified monitoring across all services becomes difficult
Solution Approach 1:
The patent creates a universal multidimensional monitoring framework that can accommodate multiple service technologies and data sources through a common structure. The system uses technology-agnostic dimensional categories (resource, service, application, host, container, process) that can represent monitoring data from any technology stack, enabling unified monitoring while preserving technology-specific details within the appropriate dimensional contexts.
Solution Approach 2:
The patent transforms monitoring data from various specialized formats into a unified parameter-based dimensional structure. By converting diverse data representations into standardized dimensional parameters (time, resource metrics, service metrics, application metrics, etc.), the system maintains the accuracy needed for technology-specific monitoring while achieving unified cross-technology visibility through parameter standardization.
Data Source
AI summary
Technologies are disclosed for the automated, rule-based generation of models from arbitrary, semi-structured observation data. Context data of received observation data, like data describing the location of on which a phenomenon was observed, is used to identify related observations, to generate entities in a model describing the observed data and to assign observations to model data. Mapping rules may be used for the on-demand generation of models, and different sets of mapping rules may be used to generate different models out of the same observation data for different purposes. Further, observation time data may be used to observer the temporal evolution of the generated model. Possible use cases of the so generated models include the interpretation of observation data that describes unexpected operation conditions in view of the generated model, or to determine how a monitored system reacts on changing conditions, like increased load.