R-tree Index Structure for Sensor Tag Data Partitioning
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Solution Overview
Problem
Existing technologies face challenges in efficiently visualizing and processing large volumes of time series sensor data due to high disk I/O and CPU computations, making real-time searching and statistical computations impractical, especially when dealing with sensor data that generates enormous amounts of data at high speeds.
Innovation Solution
An optimized index structure and method for storing and searching time series sensor tag data, involving partitioning of data into records, generating indexes, merging index files, and using a processor to determine partitions and corresponding indexes based on time segment information and tag names, allowing for high-speed data input and indexing, and minimizing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor tag data is stored and searched using existing indexing methods, then data can be retrieved, but a considerable amount of disk I/O occurs leading to degraded performance
Solution Approach 1:
The patent applies parameter changes by transforming sensor data from discrete tag-value pairs into a continuous spatial representation using R-tree indexes. This transformation changes the data structure from a non-spatial format to a spatial one, enabling efficient geometric queries and reducing disk I/O operations during data retrieval
Solution Approach 2:
The patent introduces R-tree indexes as an intermediary data structure between the raw sensor data and the query processing system. This intermediary structure organizes sensor data in a hierarchical spatial framework, allowing the system to quickly eliminate irrelevant data regions and access only the necessary data, thereby reducing disk I/O
2Productivity
If real-time searching and statistical computations are performed on sensor data, then data visualization can be achieved, but the process is virtually impossible even with powerful big data processing systems
Solution Approach 1:
The patent segments sensor data into time-series partitions and organizes them using R-tree indexes. This segmentation allows the system to process and query specific time ranges independently, enabling real-time searching and statistical computations by dividing the large dataset into manageable segments that can be processed in parallel
Solution Approach 2:
The patent performs preliminary indexing of sensor data using R-tree structures before querying. This preliminary organization of data into spatial-temporal indexes enables fast retrieval and statistical computations without requiring full data scanning, thus achieving real-time processing capabilities
3Ease of manufacture
If sensor data is stored in discrete tag-value format, then data can be easily stored, but searching requires considerable disk I/O even with indexes
Solution Approach 1:
The patent transforms the storage format parameter from discrete tag-value pairs to continuous spatial coordinates using R-tree indexes. This parameter change maintains the simplicity of data ingestion while fundamentally improving query efficiency by enabling geometric and spatial searches that reduce disk I/O operations
Data Source
AI summary
An embodiment of the invention provides a device for searching indexes for sensor tag data. The can include a memory that has sensor tag data stored as a time series; an interface that is configured to receive tag names and time segment information for searching sensor tag data; and a processor that is configured to determine partitions of the memory having the sensor tag data stored therein based on the time segment information and configured to determine corresponding indexes and leaf nodes for the determined partitions based on the tag names and the time segment information. An embodiment of the invention can provide an optimized index structure for and a method of storing and searching time series sensor tag data with which most query patterns relating to time series sensor tag data can be satisfied.