Analytics Workflow Similarity Analysis Bypass
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Solution Overview
Problem
Existing analytics workflows face challenges with high computational intensity, latency, and storage requirements due to the need for extensive ETL operations on large volumes of data, especially when dealing with similar data sets.
Innovation Solution
The proposed solution involves determining an optimal bypass stage in an analytics workflow, performing a similarity analysis to compare insights data at this stage with stored insights data, and based on the analysis, either bypassing or executing the remainder of the analytics workflow to reduce unnecessary processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ETL operations are performed on large amounts of raw data to maximize benefits, then data processing completeness is improved, but computational intensity and latency increase
Solution Approach 1:
The system performs preliminary actions by storing insights data from previous analytics workflow executions. When new raw data arrives, the system compares insights generated from this data against stored insights to identify similarities before committing to full ETL processing, thereby avoiding unnecessary computational overhead while maintaining processing completeness when needed.
Solution Approach 2:
The system creates copies of insights data from previous analytics workflow executions and stores them in an insights data repository. These copied insights are then used for comparison against new insights data to determine whether full ETL processing is necessary, enabling the system to bypass redundant processing while maintaining data integrity.
2Reliability
If ETL operations are performed on large amounts of data to maximize benefits, then data analysis accuracy is improved, but computational intensity increases
Solution Approach 1:
The system performs preliminary comparison of insights data against stored insights before initiating full ETL operations. This preliminary action identifies redundant processing opportunities, allowing the system to maintain data analysis accuracy through selective ETL execution while significantly reducing computational intensity by bypassing redundant transformations.
Solution Approach 2:
The system uses copied and stored insights data as a reference to avoid redundant ETL processing. By comparing new insights against stored copies, the system determines when full computational processing is unnecessary, thereby maintaining analysis accuracy while reducing energy consumption.
3Reliability
If extensive ETL operations are performed on large volumes of data, then data processing thoroughness is improved, but storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential insights data from analytics workflow executions in an insights data repository, rather than storing all processed data. This extraction approach maintains processing thoroughness by preserving key insights while significantly reducing storage requirements by eliminating redundant data retention.
Solution Approach 2:
The system creates selective copies of insights data for storage rather than retaining all processed data volumes. This copying strategy enables the system to maintain thorough processing capabilities through reference to stored insights while minimizing storage requirements by storing only essential comparative data.
4Reliability
If full analytics workflow is executed on all raw data to ensure accurate processing, then processing accuracy is maintained, but I/O operations increase
Solution Approach 1:
The system performs preliminary comparison of new insights data against stored insights before initiating full analytics workflow execution. This preliminary action identifies cases where data is sufficiently similar to previous datasets, enabling the system to bypass redundant I/O operations while maintaining processing accuracy through selective workflow execution.
Solution Approach 2:
The system uses stored copies of insights data to determine when full analytics workflow execution is unnecessary. By comparing new insights against stored copies, the system identifies redundant processing opportunities and bypasses them, thereby reducing I/O operations while maintaining processing accuracy through selective execution.
Data Source
AI summary
Examples include bypassing a portion of an analytics workflow. In some examples, execution of an analytics workflow may be monitored upon receipt of a raw data and the execution may be interrupted at an optimal bypass stage to obtain insights data from the raw data. A similarity analysis may be performed to compare the insights data to a stored insights data in an insights data repository. Based, at least in part, on a determination of similarity, a bypass operation may be performed to bypass a remainder of the analytics workflow.