Time-Slice Dataset Segmentation for Query Optimization
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Solution Overview
Problem
Managing large volumes of time-series data generated from various sources, such as IoT devices, is challenging due to irregular data transmission and storage requirements, making it difficult to efficiently search and filter the data while maintaining organization and accessibility.
Innovation Solution
The method involves generating multiple time-slice datasets from a time-series dataset, each with a corresponding time-slice time index, which includes field-value data strings and pointers to earlier time-slice datasets, allowing for reduced access to the original dataset during queries, thereby improving search and filtering efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the original time-series dataset is directly accessed for querying, then data completeness is maintained, but query time and computational resources increase significantly
Solution Approach 1:
The time-series dataset is segmented into multiple time-slice datasets, where each time-slice dataset contains only the data relevant to a specific time period. This segmentation allows queries to be performed on smaller, more manageable subsets of data rather than the entire dataset, significantly reducing query time and computational resources while maintaining data completeness through the organized structure of multiple slices
Solution Approach 2:
The time-slice datasets are pre-organized and stored in a structured format with clear time-based delimiters before queries are executed. This preliminary organization of data into time-based slices enables faster query execution by eliminating the need to scan through the entire dataset during query operations, as the relevant data is already segmented and ready for efficient access
2Productivity
If time-slice datasets are generated and stored separately, then query efficiency improves, but storage space requirements increase
Solution Approach 1:
Instead of creating completely separate duplicate copies of the entire dataset for each time slice, the system creates time-slice datasets that contain only the relevant portions of the original data for each time period. This selective copying approach maintains data retrieval efficiency by organizing data into accessible slices while minimizing storage space requirements by avoiding redundant data duplication
Solution Approach 2:
The time-slice datasets are structured in a nested hierarchy where each slice contains data for a specific time period, and slices can be further organized into larger time-based groups. This nesting allows the system to efficiently manage and store data at multiple levels of granularity, improving retrieval efficiency for both small and large time ranges while optimizing storage space by avoiding redundant storage of the same data at multiple levels
3Ease of operation
If the time-series dataset is organized with detailed time indexing, then data accessibility improves, but data structure complexity increases
Solution Approach 1:
The time indexing structure is segmented into discrete time-slice datasets, where each slice has its own simplified time index ranging from a start time to an end time. This segmentation transforms a single complex indexing structure into multiple simpler, manageable indexes, improving data accessibility by allowing direct access to specific time periods while reducing the overall complexity of the indexing system
Solution Approach 2:
The time indexing is transformed from a single-dimensional continuous index into a multi-dimensional structure with time-slice boundaries as an additional organizing dimension. Each time-slice dataset is identified by its start and end times, creating a two-dimensional indexing approach that improves accessibility by allowing queries to target specific time ranges directly, while the regular slicing pattern actually reduces overall structural complexity compared to managing a single large continuous index
Data Source
AI summary
A method includes generating from a time-series dataset multiple corresponding time-slice datasets. Each time-slice dataset has a corresponding time-slice time index and includes field-value data strings and associated field-value-time-index data strings, or pointers indicating the corresponding strings in an earlier time-slice dataset, that are the latest in the time-series dataset that are also earlier than the corresponding time-slice time index. A query of the time-series dataset for latest data records earlier than a given query time index is performed by using the time-slice datasets to reduce or eliminate the need to directly access or interrogate the time-series dataset.