This Server Administration course will show you how to configure an adapter server. You will learn techniques for balancing performance and resource utilization using the Server Web Console, debugging and diagnostic tips and techniques, and how to control your server environment on a daily basis. You will also learn how to implement security models to protect and manage privileges based on user role.
You will learn how to:
When creating a Data Flow, you can easily run predictive analytics on your data sets using Machine Learning functions, without prior knowledge of advanced statistics.
Train and run multiple iterations of predictive models in parallel, evaluate and compare models actively, and select which model you want to save. Then you can re-run your model against new data sets.
When creating a Data Flow, you can easily run predictive analytics on your data sets using Machine Learning functions, without having prior knowledge of advanced statistics.
From the side panel, click Models.
The Regression panel opens, and displays the following regression model algorithms:
• K-Nearest-Neighbors
• Polynomial
• Random Forest
• and Extended Gradient Boosting
Drag and drop a model onto the workflow canvas.
This content provides information about the building and integration of compiled and linked 3GL stored procedures and subroutines (DLLs) for use within FOCEXEC applications.
When you have made all of the data preparation transformations for a flow, and have added your target, on the toolbar click the Run button (
).
When the data is loaded, a report similar to the following displays.
You can create a Business View for the target to categorize the columns into measures and dimensions. This view of the data is used by Designer for creating reports and charts.
Right-click the target and select Edit Business View. A default Business View is created, as shown in the following image.
Once a data flow contains all of the fields you need, you can load it to a target data store.
To add a new target:
Using Citi Bike trip data, we will restrict our analysis to rides that start in Manhattan, so we can limit the data loaded to just that borough. The geographic data identifies the county, so we will just load the data in New York County.
The display changes to reflect the selection. The dark portions of the bars in each column show the proportion of rows that are selected, as shown in the following image.
The trip data contains station names, IDs, latitudes, and longitudes, but no zip code, city, or county information. To see the zip code and county information, you can download the station station_zip.csv file from http:/techsupport.informationbuilders.com/public/station_zip.csv.
You can add new fields or replace existing fields by creating expressions.
Each filed has a menu icon (
)
The menu provides the following options:
