Overlap Query Refinement for Joinable Dataset Recommendations
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Solution Overview
Problem
In a data marketplace with a large number of datasets, finding datasets that are joinable is a cumbersome task for users, requiring significant time and effort to confirm congruent schemas and matching key fields for effective data enrichment.
Innovation Solution
An overlap data system that categorizes datasets semantically, computes similarity functions, and recommends joinable datasets based on similarity scores, user feedback, and machine learning models to facilitate efficient data enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of congruent schemas and matching key fields is performed, then data combination accuracy is maintained, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system pre-computes and stores metadata about datasets including schema information, data types, and potential join keys. This preliminary organization allows the system to quickly retrieve and compare dataset characteristics without performing manual analysis during the data enrichment process, thus maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical processes of schema comparison and key field matching with automated computational systems. The system uses algorithms to automatically compare schemas, identify congruences, and match key fields based on data types and statistical properties, eliminating the need for manual intervention while preserving accuracy.
2Measurement precision
If comprehensive schema comparison and key field matching are performed, then joinable dataset identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system divides the complex task of schema comparison into multiple independent stages: first comparing data types, then evaluating statistical properties, and finally assessing semantic compatibility. This segmentation allows each sub-task to be processed independently with optimized algorithms, reducing overall computational complexity while maintaining comprehensive analysis for accurate identification.
Solution Approach 2:
The patent transforms the complex schema comparison problem into a series of parameter-based evaluations including data type compatibility, statistical distribution matching, and key field correlation. By changing the approach from holistic schema analysis to parameter-wise comparison, the system reduces computational complexity while preserving identification accuracy through multi-parameter validation.
3Productivity
If automated join process is implemented, then data enrichment efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements automated self-service functionality where the join process executes automatically based on pre-computed metadata and user preferences. The system autonomously identifies joinable datasets, selects appropriate join keys, and performs the join operation without requiring manual configuration or intervention, thereby improving efficiency while managing complexity through automation rather than manual procedures.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors join operation results and uses this information to refine future join recommendations. By analyzing successful and unsuccessful join attempts, the system learns from experience and improves its automated decision-making, enhancing efficiency while managing complexity through adaptive learning rather than rigid complex rules.
Data Source
AI summary
An advanced system for refining overlap queries in a database system based on user feedback. The system monitors interactions of a first user with a first dataset on the database system, where the first dataset is associated with the first user. Feedback regarding the quality of a results dataset, generated from an executed overlap query, is received from the first user. This feedback informs the generation of a similarity score dataset that enhances the creation of new overlap queries. These new overlap queries are designed to output refined overlap datasets between the first dataset and a second dataset associated with a second user. A new joined dataset is generated by executing these overlap queries, comprising data from both the first and second datasets. A new results dataset is generated, providing the first user with refined recommendations based on additional feedback.