Invention Grant
- Patent Title: Primary key-foreign key relationship determination through machine learning
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Application No.: US15210889Application Date: 2016-07-15
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Publication No.: US10692015B2Publication Date: 2020-06-23
- Inventor: Yongming Xu , Ram Dayal Goyal
- Applicant: lo-Tahoe LLC
- Applicant Address: US NY New York
- Assignee: Io-Tahoe LLC
- Current Assignee: Io-Tahoe LLC
- Current Assignee Address: US NY New York
- Agency: M&B IP Analysts, LLC
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F16/21

Abstract:
A method and a machine learning relationship determination system (MLRDS) for determining primary key-foreign key (PK-FK) relationships among data in tables of a target database through machine learning (ML) are provided. The MLRDS selects columns of the tables in the target database and identifies inclusion dependency (ID) pairs from the selected columns. The MLRDS receives training data and validation data from a source database, computes PK-FK features for the inclusion dependency pairs, the training data, and the validation data, and generates trained ML models and validated ML models using the PK-FK features. The MLRDS determines an optimum algorithm decision threshold for a selected machine learning classification algorithm (MLCA), using which the MLRDS determines a resultant on whether the inclusion dependency pair is a PK-FK pair or a non-PK-FK pair. The MLRDS performs majority voting on the resultant for multiple MLCAs to confirm the PK-FK relationships between the inclusion dependency pairs.
Public/Granted literature
- US20180018579A1 Primary Key-Foriegn Key Relationship Determination Through Machine Learning Public/Granted day:2018-01-18
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