Method and system for analyzing data in a database
A method and system analyze data in a database. The method and system include defining a plurality of set of rules, wherein each set of rules corresponds to a predictive model, storing the each set of rules corresponding to the predictive model in a library as a user-defined function, and calling the user-defined function via a standard sequel language.
1. A system for analyzing data in a database, the method system comprising:
one or more processors; and
a memory, coupled to the one or more processors, having code that when executed by the one or more processors to perform operations comprising:
generate a database query; and
receive a response to the database query, wherein the response is generated by the one or more processors performing operations comprising:
store a plurality of sets of rules corresponding to a predictive model in a library, wherein each set of the rules corresponds to the predictive model, wherein:
each set of the rules is stored as a user-defined function in the library;
each set of the rules is defined to correspond to the predictive model; and
the predictive model is built using a training data set to find a probability of an advertisement being clicked; and
the training data set includes multiple features used as input parameters to train the predictive model for classification of values;
call the user-defined function in a database query, wherein the user defined function is called to analyze the data in the database;
invoke the user-defined function to operate on one or more rows in the database directly from a function-calling module to analyze the data in the database in accordance with the predictive model;
process the data responsive to the database query with the user-defined function to generate the response to the user-defined function; and
provide the response indicating the probability of the advertisement being clicked.
2. The system as claimed in claim 1 , wherein the user defined function is configured to operate on one or more rows in the database.
3. The system as claimed in claim 1 , wherein the user-defined function is called via a standard sequel language.
4. The system of claim 1 , wherein the features of the training data set include an advertisement type.
5. The system as claimed in claim 1 , wherein to call the user-defined function in a database query the function-calling module comprises to apply the called user defined function on one or more rows in the database.
6. The system as claimed in claim 1 , wherein the user-defined function is called via a structured query language.
7. A non-transitory, computer readable medium storing code for analyzing data in a database, wherein when the code is configured to be executed by a one or more processors to cause the one or more processors to perform operations comprising:
generate a database query; and
receive a response to the database query, wherein the response is generated by the one or more processors performing operations comprising:
store a plurality of sets of rules corresponding to a predictive model in a library, wherein each set of the rules corresponds to the predictive model, wherein:
each set of the rules is stored as a user-defined function in the library;
each set of the rules is defined to correspond to the predictive model; and
the predictive model is built using a training data set to find a probability of an advertisement being clicked; and
the training data set includes multiple features used as input parameters to train the predictive model for classification of values;
call the user-defined function in a database query, wherein the user defined function is called to analyze the data in the database;
invoke the user-defined function to operate on one or more rows in the database directly from a function-calling module to analyze the data in the database in accordance with the predictive model;
process the data responsive to the database query with the user-defined function to generate the response to the user-defined function; and
provide the response indicating the probability of the advertisement being clicked.
8. The non-transitory, computer readable medium of claim 7 , wherein the features of the training data set include an advertisement type.
9. The non-transitory, computer readable medium of claim 7 , wherein the features of the training data set include an advertisement type.
10. The non-transitory, computer readable medium of claim 7 , wherein the user-defined function is called via a standard sequel language.
11. The non-transitory, computer readable medium of claim 7 , wherein the user-defined function is called via a structured query language.
12. The non-transitory, computer readable medium of claim 7 , wherein to call the user-defined function in a database query the function-calling module comprises to apply the called user defined function on one or more rows in the database.