Industrial Analysis Tool Ranking via Metadata Filtering
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Solution Overview
Problem
Existing toolsets for data analysis in industrial applications face challenges in identifying optimal combinations of data analysis tools and data sets due to the impracticality of evaluating all available combinations, leading to unmet needs in achieving accurate and useful results.
Innovation Solution
A system and method for ranking search results of industrial analysis tools and data sets using metadata tags to characterize tool and data set characteristics, enabling users to specify search criteria and score compatible combinations based on quality criteria, thereby identifying the best combinations for accurate and useful results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all available combinations of data analysis tools and data sets are evaluated, then optimal combinations can be identified, but the complexity and time required becomes impractical
Solution Approach 1:
The patent segments the evaluation process into two distinct phases: (1) a filtering phase that uses metadata tags to quickly eliminate incompatible tool-dataset combinations, and (2) a detailed evaluation phase that assesses only the filtered subset of compatible combinations. This segmentation resolves the contradiction by reducing the complexity of the overall evaluation process while maintaining reliability through systematic filtering.
Solution Approach 2:
The patent applies preliminary action by performing metadata-based filtering before the actual evaluation of tool-dataset combinations. The metadata tags are预先 assigned to tools and datasets, enabling the system to pre-filter compatible combinations before conducting detailed assessment. This preliminary filtering action reduces the search space and makes the evaluation process practical while ensuring optimal combinations are not missed.
2Reliability
If all available combinations of data analysis tools and data sets are evaluated, then optimal combinations can be identified, but the time required becomes impractical
Solution Approach 1:
The evaluation process is segmented into a fast metadata filtering stage and a detailed evaluation stage. The metadata filtering stage quickly eliminates incompatible combinations without requiring time-consuming detailed assessment, thus significantly reducing the total evaluation time while maintaining accuracy through systematic filtering of the remaining candidates.
Solution Approach 2:
Metadata tags are used to perform preliminary filtering of tool-dataset combinations before detailed evaluation. This preliminary action identifies and eliminates incompatible combinations in advance, reducing the number of combinations that require time-consuming detailed assessment and thereby reducing the overall evaluation time while preserving accuracy.
3Productivity
If metadata tags are used to filter combinations, then evaluation efficiency is improved, but the precision of matching may be reduced
Solution Approach 1:
The matching process is segmented into two levels: (1) metadata-based filtering that uses tags for efficient preliminary screening, and (2) detailed compatibility assessment that evaluates the filtered combinations against specific compatibility criteria. This segmentation improves productivity through fast metadata filtering while maintaining precision through the subsequent detailed assessment stage.
Solution Approach 2:
Metadata tags perform preliminary filtering to identify potentially compatible tool-dataset combinations. This preliminary action improves efficiency by eliminating obviously incompatible combinations quickly. The precision is maintained because the filtered combinations still undergo detailed compatibility assessment to ensure accurate matching.
Data Source
AI summary
Unique systems, methods, techniques and apparatuses for ranking and applying industrial analysis search results are disclosed. One exemplary embodiment is a computer implemented method comprising receiving a request to rank potential combinations of the data set and the plurality of data analysis tools; tagging the plurality of data analysis tools with one or more metadata tags selected from a plurality of metadata tags; tagging the data set with one or more metadata tags selected from the plurality of metadata tags; scoring a plurality of potential combinations of the data analysis tool and the plurality of data sets according to correspondence between the metadata tags tagged to the data analysis tool and the metadata tags tagged to each of the plurality of data sets; and outputting a user perceptible ranking of the plurality of potential combinations indicating the scoring.