Abstract: An LED lighting device includes a seat (1), a first optical member (3), a first light source (21) and a second light source (22). The seat (1) includes a bottom portion (11) and a side portion (12). The first optical member (3) is disposed on the seat (1) and covers the bottom portion (11). The first light source (21) is disposed between the bottom portion (11) of the seat (1) and the first optical member (3). The second light source (22) is disposed on the side portion (12) of the seat (1).

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.