Adaptive Sampling Rate Selection for Distributed Data Processing

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Solution Overview

Problem

Existing methods for processing large data sets are resource-intensive and time-consuming, and rely on manual selection of sampling rates, which is not suitable for automated applications, especially when datasets are frequently updated.

Innovation Solution

An adaptive method that automatically selects a suitable sampling rate by partitioning data into unbalanced buckets, assigning them to processing units, and determining a sampling error threshold to ensure efficient and accurate results for subsequent data processing jobs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual selection of sampling rates is used, then simplicity of implementation is maintained, but adaptability to frequently updated datasets deteriorates

Engineering Contradiction:

Improveadaptability to frequently updated datasetsVSAvoidcomplexity of adaptive sampling rate selection

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically selecting sampling rates without manual intervention. The adaptive sampling rate selection mechanism monitors data characteristics and autonomously adjusts sampling parameters, allowing the system to adapt to frequently updated datasets while maintaining operational simplicity through automation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where sampling results are continuously evaluated and fed back into the sampling rate selection process. This feedback loop enables the system to learn from previous sampling outcomes and dynamically adjust sampling rates to optimize performance on frequently updated datasets.

Inventive Principle:

Principle #23Feedback

2Productivity

If sampling rates are manually selected, then implementation simplicity is maintained, but productivity of automated applications deteriorates

Engineering Contradiction:

Improveproductivity of automated applicationsVSAvoidease of sampling rate selection

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system eliminates the need for manual sampling rate selection by implementing self-service automation. The adaptive sampling rate selection mechanism automatically determines optimal sampling parameters based on data characteristics, significantly improving productivity for automated applications while the standardized implementation maintains ease of operation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of data characteristics before executing sampling operations. By pre-evaluating data patterns and determining optimal sampling rates in advance, the system enhances productivity for automated applications while maintaining ease of operation through pre-configured sampling strategies.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If full data sets are processed, then measurement precision is maintained, but resource consumption increases

Engineering Contradiction:

Improveprecision of query resultsVSAvoidresource consumption of data processing

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system dynamically changes sampling rate parameters based on data characteristics and query requirements. By adjusting the sampling rate parameter, the system processes only the necessary subset of data, maintaining measurement precision for critical queries while reducing resource consumption for less critical operations.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial action by processing only a sampled subset of the full dataset rather than the complete data set. This selective processing maintains sufficient measurement precision for most queries while significantly reducing resource consumption, applying excessive action only when query criticality demands full data processing.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11182376B1Identifying variance in distributed systems

Publication Date: 2021.11.23 QUANTCAST CORP

AI summary

Adaptive Sampling. Data comprising pairings of data value with lists of data keys are received. The range of possible values of the data keys is partitioned into unbalanced buckets, with at least two of the unbalanced buckets representing different fractions of the range. Each unbalanced bucket is assigned to a respective processing unit selected from a plurality of processing units. The pairings are processed by the processing units, with each processing unit generating an intermediate result. The intermediate results are combined to generate a comprehensive result. A sampling error is determined by scaling an unbalanced bucket's intermediate result according to its corresponding fraction and comparing the scaled intermediate result to the comprehensive result. An unbalanced bucket having a sampling error less than a sampling error threshold is selected. The selected unbalanced bucket's corresponding fraction is selected as a sampling rate for a second data processing job.