Data Modeling Assistant for Semantic Join and Reuse Recommendations
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Solution Overview
Problem
Current business intelligence systems require users to have extensive schema knowledge and manual effort to join tables correctly, leading to duplicate datasets, data redundancy, and inefficient dataset modification, without automated tools for preventing duplication or recommending appropriate joins.
Innovation Solution
A data modeling assistant that provides real-time recommendations for dataset duplication prevention, join suggestions, and dataset reuse by utilizing semantic profiling and heuristics to compare incoming datasets with existing metadata, generating similarity scores and providing ranked suggestions for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual table joining and schema knowledge are required, then data accuracy can be maintained, but user productivity decreases and operation complexity increases
Solution Approach 1:
The system performs self-service by automatically generating dataset recommendations and join suggestions through semantic profiling and metadata comparison, eliminating the need for users to manually join tables or possess extensive schema knowledge. The system autonomously analyzes incoming datasets against existing metadata to provide actionable recommendations.
Solution Approach 2:
The patent replaces manual mechanical operations (user-driven table joining and schema analysis) with automated computational processes. Semantic profiling algorithms and metadata comparison mechanisms substitute for human expertise, transforming the manual process into an automated recommendation system that enhances productivity while reducing operational complexity.
2Productivity
If automated recommendations are provided, then productivity increases, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing metadata for existing datasets before they are needed for recommendation. This advance preparation of semantic profiles and metadata structures enables rapid automated recommendations without requiring complex real-time analysis, thus improving productivity while managing system complexity through proactive data preparation.
3Reliability
If metadata comparison is performed, then dataset duplication is reduced, but processing time increases
Solution Approach 1:
The system extracts only the essential metadata elements required for duplication detection from incoming datasets, rather than performing comprehensive full-data analysis. By selectively comparing key metadata fields and semantic profiles, the system effectively reduces dataset duplication while minimizing the time penalty associated with metadata comparison processing.
Data Source
AI summary
Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to provide a data modeling assistant for use with the data analytics environment. A method can provide, by a computer including one or more processors, access to a data analytics environment. The method can receive, at the data analytics environment, an instruction to ingest a first dataset, the first data set being retrieved from a computing device or from a storage accessible by the data analytics environment. The method can semantically profile, during the data analytics environment and during ingestion of the first dataset, the first dataset to generate a set of metrics and metadata associated with the first dataset. The method can generate a recommendation for the first dataset.