Adaptive Data Sampling for Query Estimation
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Solution Overview
Problem
As data sets grow larger, identifying responsive data objects becomes resource-intensive, and sampling a subset is necessary, but ensuring the subset accurately represents the full data set is challenging, especially when data sets are not randomly organized, leading to potential biases in estimation.
Innovation Solution
A method involving stepping through the data set with a variable step size, adjusting based on the number of responsive objects found, to create a representative subset for search queries, allowing for accurate estimation of responsive objects without exhaustive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a subset of data objects is sampled to reduce resource consumption, then resource efficiency improves, but measurement precision deteriorates due to potential non-representativeness of the subset
Solution Approach 1:
The step size N is dynamically adjusted during the sampling process based on the count of responsive objects observed so far. When the count exceeds expected thresholds, the step size is reduced to increase sampling density, and when the count is below thresholds, the step size is increased to reduce sampling effort. This dynamic adjustment allows the system to adapt to the actual data distribution and achieve accurate estimates while minimizing resource consumption.
Solution Approach 2:
The system continuously monitors the count of responsive objects during sampling and uses this feedback to adjust the step size N in real-time. The feedback mechanism compares the observed count against expected values and modifies the sampling strategy accordingly, ensuring that the subset remains representative of the full data set while optimizing resource usage.
2Measurement precision
If a fixed small step size is used to ensure representative sampling, then measurement precision improves, but productivity deteriorates due to increased sampling time
Solution Approach 1:
The step size transitions from a fixed value to a dynamic parameter that adjusts during execution. The system starts with an initial step size and modifies it based on real-time observations of responsive object counts. This allows the sampling process to be more aggressive (larger steps) when the data suggests fewer responsive objects, and more conservative (smaller steps) when more responsive objects are detected, thereby balancing accuracy and speed.
Solution Approach 2:
The system performs partial sampling rather than exhaustive analysis, using adaptive step sizes to determine the appropriate level of sampling effort. When the adaptive logic determines that the current sample is sufficient to achieve the desired estimation accuracy, the sampling process can be terminated early, avoiding unnecessary additional sampling operations and preserving productivity.
3Productivity
If the step size N is increased to reduce sampling effort, then productivity improves, but measurement precision deteriorates due to larger gaps between sampled objects
Solution Approach 1:
The feedback mechanism monitors the distribution and count of responsive objects during sampling. When large step sizes cause the sample to become non-representative (detected through statistical checks or threshold comparisons), the system reduces the step size to improve representativeness. This feedback loop ensures that productivity gains from larger step sizes do not compromise measurement precision.
Solution Approach 2:
The step size is transformed from a static parameter to a dynamic one that responds to the characteristics of the data being sampled. The system adjusts N based on observed data patterns, increasing it when the data suggests uniform distribution and decreasing it when clustering or non-uniformity is detected, thereby maintaining measurement precision while maximizing productivity.
Data Source
AI summary
In one embodiment, a method includes receiving a search query by one or more computing devices. A set of data objects to search are identified, and then an operation is executed. The operation involves stepping through the set of data objects with a step size of N, and at each step determining whether the object is responsive to the query. If the end of the set is reached before Y data objects are determined to be responsive, and X or more data objects are determined to be responsive, then an estimate of the number of responsive data objects in the set is provided. Otherwise, N is modified and the operation is re-executed.