Time-Series Data Aggregation for Subject Distribution Analysis
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Solution Overview
Problem
Existing techniques for analyzing time-series data from images struggle to reduce data volume while maintaining analysis accuracy, leading to potential degradation in the analysis of a specific subject's distribution across multiple images captured over a predetermined period.
Innovation Solution
An information processing apparatus and method that acquire time-series data, generate data sets for various time widths, estimate analysis accuracy, determine optimal aggregation time widths based on data reduction and accuracy, and perform aggregation to reduce data volume without degrading analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If aggregation is performed on time-series data to reduce data volume, then data amount is reduced, but analysis accuracy deteriorates
Solution Approach 1:
The patent segments the time-series data into multiple data sets based on different time widths (e.g., 5 seconds, 10 seconds, 15 seconds). By dividing the data into segments with different granularities, the system can select an appropriate time width that balances data reduction with maintaining analysis accuracy for detecting subject distribution patterns.
Solution Approach 2:
The patent dynamically determines the optimal time width for aggregation based on the specific characteristics of the time-series data and the analysis requirements. The system adjusts the aggregation parameters adaptively to maintain analysis accuracy while reducing data volume, rather than using a fixed aggregation method.
2Quantity of substance
If time width for aggregation is increased, then data reduction degree improves, but analysis accuracy deteriorates
Solution Approach 1:
The patent changes the time width parameter to control the balance between data reduction and analysis accuracy. By adjusting this parameter, the system can optimize the aggregation process to achieve the desired data reduction while maintaining sufficient accuracy for detecting subject distribution patterns in the target space.
Solution Approach 2:
The patent performs preliminary analysis to determine the optimal time width before actual aggregation. By pre-calculating and evaluating different time width options, the system can select the best aggregation parameters in advance, ensuring that data reduction is achieved without compromising analysis accuracy.
Data Source
AI summary
An information processing apparatus (100) includes: an acquisition unit (110) configured to acquire first time-series data including presence area information in a target space in subjects; a data set generation unit (120) configured to generate a plurality of data sets from presence area information regarding a specific subject; an estimation unit (130) configured to estimate an accuracy of analyzing a distribution regarding the subject in the target space based on presence area information; a determination unit (140) configured to determine a time width of an aggregation target based on the data reduction degree due to the aggregation in each data set and an accuracy of analysis; and an aggregation unit (150) configured to perform aggregation on a data set that corresponds to the determined time width to representative data.