Big Data Sampling for Preprocessing Transformation Scripts
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Solution Overview
Problem
Conventional sampling techniques for big data preprocessing are inadequate, often failing to represent the entire dataset, leading to incomplete transformation scripts that cannot handle anomalies or specific data operations, resulting in limited data processing capacity and poor analysis quality.
Innovation Solution
A data preprocessing system that determines and applies transform operations to datasets, generating sample sets for testing and development, and visualizing data distribution to identify and store relevant input records, thereby improving the quality of transformation scripts and increasing the amount of processable data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sampling techniques (random sampling or reading first few rows) are used, then the sampling process is simple and fast, but the sample data does not represent the entire dataset and lacks necessary features for testing transformation scripts
Solution Approach 1:
The system performs preliminary analysis of the entire dataset to identify key features, anomalies, and data characteristics before selecting sample records. This preliminary action ensures that the sampled data contains representative features for testing transformation scripts, resolving the contradiction between sample representativeness and sampling complexity.
Solution Approach 2:
The system uses feedback from analyzing the full dataset to guide the sampling process. By first examining data features, anomalies, and distribution patterns, the system adjusts sample selection to ensure representativeness, thereby improving sample quality without excessively increasing complexity.
2Reliability
If the entire dataset is used to develop transformation scripts, then the scripts can handle all data features and anomalies, but the processing time and computational resources are excessive
Solution Approach 1:
The system performs a quick preliminary analysis of the entire dataset to identify key features, anomalies, and critical data patterns. Based on this preliminary action, it selects a representative sample that contains all necessary features for developing complete transformation scripts, thus avoiding the need to process the entire dataset while ensuring script completeness.
Solution Approach 2:
The system extracts only the essential features, anomalies, and critical data patterns from the entire dataset that are necessary for testing transformation scripts. By taking out and focusing on these key elements rather than processing all data, the system achieves complete script development with reduced time and computational resources.
3Productivity
If transformation scripts are developed without proper sampling, then the development process is faster, but the scripts fail to handle anomalies and specific data operations present in the full dataset
Solution Approach 1:
The system performs a rapid preliminary analysis to identify critical anomalies and data features that must be handled by transformation scripts. This preliminary action enables developers to create robust scripts faster by focusing immediately on the most important test cases rather than randomly sampling data.
Solution Approach 2:
The system provides feedback about the full dataset's characteristics, anomalies, and data quality issues to guide the sampling and script development process. This feedback mechanism ensures that transformation scripts are developed with awareness of all critical data features, improving robustness while maintaining development speed.
Data Source
AI summary
A system determines samples of datasets that are typically processed by big data analysis systems. The samples are for use for development and testing of transformations for preprocessing the datasets in preparation for analysis by big data systems. The system receives one or more transform operations input datasets for the transform operations. The system determines samples associated with the transform operations. According to a sampling strategy, the system determines samples that return at least a threshold number of records in the result set obtained by applying a transformation. According to another sampling strategy, the system receives criteria describing the result of the transform operations and determines sample sets that generate result sets satisfying the criteria as a result of applying the transform operations.