Multi-Dimensional Time Series Data Middleware for Real-Time Analysis
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Solution Overview
Problem
Existing systems struggle with efficiently analyzing large volumes of multi-dimensional time series data in real-time, particularly in mission-critical applications, due to the time-consuming nature of data analysis and the need for substantial computational resources.
Innovation Solution
A middleware analysis platform that performs real-time retrieval and computation of multi-dimensional time series data from databases, using metadata to identify data locations and generate contextual outputs, including correlation and regression analysis, while offloading computational tasks to a time series service that can handle queries and transformations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time analysis of multi-dimensional time series data is performed, then responsiveness and detection speed improve, but computational load and resource requirements increase
Solution Approach 1:
A time series service is introduced as an intermediary component between the database and analysis platforms. This service pre-processes and stores time series data in an optimized format, enabling fast retrieval without requiring heavy computational resources during real-time analysis. The intermediary handles the computational burden of data preparation, allowing multiple analysis platforms to query efficiently.
Solution Approach 2:
The system performs preliminary processing of time series data by pre-computing and storing data in an optimized structure within the time series service. This advance preparation includes organizing data for efficient querying and reducing the computational work needed during real-time analysis operations.
2Measurement precision
If computational operations are performed on large volumes of time series data, then analysis accuracy improves, but processing time increases
Solution Approach 1:
The system segments time series data into manageable chunks organized by time ranges and dimensions. The time series service divides large datasets into smaller, pre-processed units that can be quickly retrieved and combined during analysis, maintaining accuracy while reducing processing time for large-volume operations.
3Adaptability or versatility
If multiple analysis platforms query the same time series data, then system versatility improves, but database load increases
Solution Approach 1:
The time series service acts as a mediator between multiple analysis platforms and the database. It handles all query operations, data retrieval, and processing requests from multiple platforms simultaneously, preventing direct database overload while enabling versatile multi-platform analysis capabilities.
Data Source
AI summary
A method may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.