Time-Series Data Compression Using Adaptive Starting Points
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Solution Overview
Problem
The increasing volume and types of time-series data in databases demand a more efficient method for data compression to prevent deterioration in retrieval performance and storage capacity.
Innovation Solution
A data compression device that receives input data, generates multiple starting point candidates within a threshold error, selects the candidate that approximates the most intermediate data points effectively, and outputs the selected starting point and end point data for compression, enhancing the compression ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single starting point is fixed for linear approximation (Swinging Door algorithm), then the algorithm is simple to implement, but the compression ratio is limited and cannot efficiently handle large volumes of time-series data
Solution Approach 1:
The invention segments the data approximation process by dividing it into multiple linear approximation segments, each starting from a different starting point candidate. This allows the system to evaluate multiple potential starting points and select the optimal one, thereby improving compression efficiency while maintaining manageable complexity through structured segmentation of the approximation process.
Solution Approach 2:
The invention changes the parameter of starting point selection by generating multiple starting point candidates with different time points and selecting the optimal candidate based on approximation accuracy. This parameter change enables the system to adapt to different data characteristics and achieve better compression ratios compared to fixed single starting point methods.
2Productivity
If multiple starting point candidates are generated and evaluated, then the compression ratio improves, but the computational complexity increases
Solution Approach 1:
The invention performs preliminary action by pre-generating multiple starting point candidates before the main approximation process. This preliminary generation of candidates with controlled time intervals allows the system to prepare multiple options in advance, reducing the computational burden during the actual approximation and selection phases while still achieving improved compression ratios.
Solution Approach 2:
The invention applies partial action by generating a limited number of starting point candidates (not all possible points) based on predetermined time intervals and error thresholds. This partial generation approach balances computational complexity with compression effectiveness, avoiding the excessive computation that would result from evaluating all possible starting points while still achieving superior compression compared to single starting point methods.
3Measurement precision
If data thinning is performed with stricter error thresholds to maintain precision, then measurement precision is improved, but the compression ratio decreases
Solution Approach 1:
The invention changes the parameter of starting point selection to optimize the balance between precision and compression ratio. By selecting the starting point candidate that provides the best approximation accuracy within the error threshold, the system maintains measurement precision requirements while achieving higher compression ratios through more efficient data thinning decisions.
Solution Approach 2:
The invention incorporates feedback by evaluating the approximation error for each starting point candidate and using this error information to select the optimal candidate. This feedback mechanism ensures that the selected starting point maintains the required precision level while enabling more aggressive data thinning, thus improving compression ratio without sacrificing measurement precision.
Data Source
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AI summary
A data compression device includes a receiving unit, a generating unit, a selecting unit, and a compressing unit. The receiving receives a plurality of pieces of input data which is input in chronological order. The generating unit generates a plurality of starting point candidates which represents the data having an error within a threshold value with respect to starting point data. The starting point data is the input data input at a first timing. The selecting unit refers to the starting point candidates, end point data which is the input data input at a second timing, and intermediate data which is the input data input at a timing in between the first timing and the second timing; and selects, from among the starting point candidates, the starting point candidate which, as compared to the other starting point candidates, has a greater number of pieces of the intermediate data approximated using the starting point candidate and using the end point data in such a way that the error is within the threshold value. The compressing unit outputs the selected starting point candidate and the end point data as output data obtained by compressing the starting point data, the intermediate data, and the end point data.