Middleware Time-Series Analysis for Real-Time Hazard Alerts
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Solution Overview
Problem
Existing time series databases struggle with efficiently handling and analyzing large volumes of multi-dimensional time series data in real-time, particularly in mission-critical applications, due to the computational intensity and complexity of tasks such as correlation and regression analysis, which can be time-consuming and not suited for real-time analytics.
Innovation Solution
A middleware analysis platform that performs real-time retrieval and computation of multi-dimensional time series data from pre-registered databases, generating contextual outputs through correlation, regression, and derivatives, while also handling real-time streaming data cleaning, parsing, and storage in both hot and cold storage systems, and issuing alerts based on predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational operations are performed on large volumes of multi-dimensional time series data, then analysis depth and contextual understanding improve, but processing time and computational load increase significantly
Solution Approach 1:
The patent segments the computational workload by implementing a distributed computing architecture that divides multi-dimensional time series data analysis into independent computational tasks distributed across multiple processing nodes. This allows parallel processing of different data dimensions and time periods, reducing overall processing time while maintaining comprehensive analysis depth.
Solution Approach 2:
The system performs preliminary actions by pre-computing and caching intermediate results of computational operations on time series data. Frequently accessed data patterns, statistical summaries, and transformation results are stored in advance, enabling rapid retrieval and reducing real-time processing requirements while maintaining analytical depth.
2Speed
If real-time computational operations are performed on multi-dimensional time series data, then response speed improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent introduces intermediary components including a query optimization layer and result caching mechanism that mediate between data sources and computational operations. These intermediaries pre-process queries, optimize data retrieval paths, and cache intermediate results, reducing the complexity burden on core computational systems while maintaining real-time response capabilities.
3Loss of information
If comprehensive computational operations including correlation and regression are performed, then data contextualization improves, but computational load and processing time increase
Solution Approach 1:
The system applies partial action by selectively performing computational operations based on query requirements and data characteristics. Not all multi-dimensional time series datasets undergo full correlation and regression analysis - only those subsets relevant to specific analytical questions are processed with appropriate computational depth, reducing overall computational load while maintaining necessary contextualization.
Data Source
AI summary
A method can comprise accessing streaming data; monitoring the parsed streaming data to determine whether the parsed streaming data satisfies a condition; accessing historical data comprising information relating to: a historical condition previously detected and how the historical condition was resolved; determining one or more system operations performable on the one or more technical systems based on at least the information relating to how the historical condition was resolved; in response to user selection, performing at least one of the one or more system operations on the one or more technical systems.