Representation Index for Time Series Data Storage
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Solution Overview
Problem
Existing database management systems face challenges in quickly accessing and organizing multiple representations of time series data, particularly due to the increasing volume and frequency of fine-grained measurements, which requires efficient storage and processing techniques to support rapid query and analysis operations.
Innovation Solution
Implementing a representation index for time series data that includes a start row identifier, representation identifier, and offset, allowing for dynamic changes and multi-versioning, enabling efficient data operations without direct access to the raw data column, and utilizing different compression models and storage media based on data age to optimize memory consumption and processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple representations of time series data are stored using different storage approaches, then data analysis flexibility and query efficiency are improved, but storage complexity and memory consumption increase
Solution Approach 1:
The patent segments time series data into multiple representations stored in different storage approaches (e.g., memory, disk, compressed formats). Each representation serves specific query types or time ranges, allowing the system to optimize for different access patterns without managing a single complex storage structure. The index divides the data space into manageable segments that can be independently managed and accessed.
Solution Approach 2:
The patent introduces an additional dimension to the storage system by creating multiple representations of the same time series data in different formats and storage locations. Instead of storing data in a single linear structure, the system creates parallel representations (e.g., raw data, compressed data, aggregated data) that can be accessed depending on the query requirements, effectively adding a dimensional layer to the storage architecture.
2Measurement precision
If fine-grained time series measurements are stored with high sampling rates, then data precision and analysis capability are improved, but data volume and processing requirements increase
Solution Approach 1:
The patent applies local quality by storing different representations of time series data with varying levels of detail in different locations. High-precision raw measurements are stored for specific time ranges or segments where detailed analysis is needed, while other segments use compressed or aggregated representations. This allows the system to maintain measurement precision where necessary while reducing overall data volume through localized optimization.
Solution Approach 2:
The patent changes parameters of data representation by creating multiple versions of the same time series data with different compression levels, sampling rates, or aggregation granularities. The system can switch between these parameter variations depending on the query requirements, storing high-precision data only when needed while using lower-precision representations for routine operations, thereby reducing overall data volume while maintaining analysis capability.
3Speed
If a representation index is implemented to quickly access multiple data representations, then data access speed is improved, but index maintenance overhead and system complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing indexes that map query parameters to the appropriate data representations before actual queries are executed. The representation index is built and maintained in advance, organizing metadata about where different representations are stored and how to access them. This preliminary organization enables rapid query execution without requiring complex real-time data processing or search algorithms.
Data Source
AI summary
Time series data may be represented with multiple representations, optionally using a variety of storage approaches, and the plurality of representations may be indexed using a representation index, which includes a start row identifier, a representation identifier, and an offset within the representation for each segment of one or more rows in the time series data column.