Time Series Data Query Engine for Sensor Grouping
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Solution Overview
Problem
The vast quantity of time series data generated by sensors in distributed systems, such as power grids, overwhelms conventional data processing, storage, and access techniques, necessitating an efficient method for customized queries on specific subsets of this data.
Innovation Solution
A time series database system that stores data in a manner allowing for efficient retrieval of specific subsets, grouping sensors by geographic region, hierarchy level, and measurement type, and enabling customized queries with filtering and grouping based on sensor attributes, thereby supporting real-time and historical data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data processing techniques are used to handle vast quantities of time series data from sensors, then data processing capacity is maintained at standard levels, but the system becomes overwhelmed and cannot efficiently process or retrieve specific subsets of data
Solution Approach 1:
The patent segments the vast time series data into organized collections grouped by sensor attributes (geographic region, hierarchy level, measurement type). This segmentation allows the system to efficiently retrieve and process only relevant subsets of data rather than handling the entire data volume, directly resolving the contradiction between processing efficiency and data quantity.
2Ease of operation
If all time series data from all sensors is stored together without organization, then storage requirements are minimized, but retrieval of specific subsets becomes inefficient and complex
Solution Approach 1:
The patent applies local quality by organizing data storage according to specific sensor attributes (geographic region, hierarchy level, measurement type). Each data collection is stored with its specific organizational characteristics, enabling efficient retrieval of relevant subsets while maintaining a manageable overall structure. This resolves the contradiction by making retrieval easy without creating excessive complexity.
3Loss of information
If customized queries with filtering and grouping are implemented, then specific subsets of time series data can be efficiently retrieved, but the query system complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-organizing time series data into collections grouped by sensor attributes before queries are executed. This preliminary organization enables efficient filtering and grouping operations during query processing, reducing the complexity of real-time query operations while ensuring high data relevance in results.
Data Source
AI summary
In one aspect, there is provided a system configured to receive time series data collected by a first sensor. The time series data collected by the first sensor can be stored in a first data array associated with the first sensor. The first data array can stored proximate to a second data array that includes time series data collected by a second sensor. The first data array can be stored proximate to the second data array based on the first and second sensor being in a same sensor group. A query can be received to perform a processing algorithm on a subset of time series data. The subset can be generated by retrieving the first and second data array. The query can be executed by applying the processing algorithm to the subset of time series data. Related methods and articles of manufacture are also provided.