Abstract: The present disclosure relates to the technical field of intelligent driving, in particular to a vehicle driving intelligent assistance system, which includes an electronic control unit and a Controller Area Network (CAN) module, wherein the CAN module is connected with a driving detection module, a vehicle body state detection module, an engine detection module and an environment detection module, wherein the vehicle body state detection module is configured to acquire a steering angle of a steering wheel and a suspension condition, the environment detection module is configured to acquire environmental information around the vehicle and road conditions; the electronic control unit is connected with a vehicle body stabilization system, a vehicle control system and a lane departure early warning module, and the vehicle control system is configured to control acceleration, steering or braking of a vehicle to achieve assisted driving.

Abstract: A system and method for determining a relationship among data sets. The method includes selecting a first data set from a first table, and a second data set from a second table, forming an inclusion dependency pair of data based on the selected first data set and the selected second data set, determining a resultant of the inclusion dependency pair, and determining a primary key-foreign key relationship between the first data set and the second data set based on the determined resultant.

Abstract: A system and method for determining a relationship among data sets. The method includes selecting a first data set from a first table, and a second data set from a second table, forming an inclusion dependency pair of data based on the selected first data set and the selected second data set, determining a resultant of the inclusion dependency pair, and determining a primary key-foreign key relationship between the first data set and the second data set based on the determined resultant.

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.

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.

Abstract: The present invention provides a method and apparatus for providing search results, wherein the method comprises: obtaining a plurality of search results corresponding to a query request, wherein the plurality of search results comprise one or more search result classifications, each search result classification corresponds to one or more tags; determining presentation pattern information corresponding to the search result classification based on the one or more tags corresponding to the search result classification; providing at least one of the search result classifications to the corresponding user according to the corresponding presentation pattern information. The present invention helps the user to quickly find the desired information when the user's search demand is indefinite.