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1 Getting Started
This chapter provides an overview of Oracle Retail AI Foundation Cloud Services.
About Oracle Retail AI Foundation Cloud Services
The Oracle Retail AI Foundation Cloud Services is an analytical product that consists of the following modules: Customer Decision Tree Science Cloud Service, Demand Transference Science Cloud Service, Advanced Clustering Cloud Service, Customer Segmentation, Lifecycle Pricing Optimization Cloud Service and Assortment and Space Optimization Cloud Service. You may have access to all of these modules or to a subset of them.
Administration
For information about the administration of Oracle Retail AI Foundation Cloud Services, see Oracle Retail AI Foundation Cloud Services Administration Guide .
Oracle Retail AI Foundation Dashboard
The dashboard provides access to all Oracle Retail AI Foundation modules. The list of available options will be displayed upon startup.
Process Train
You can use the process train to navigate through the stages of each module. You can also use the Back button and the Next button to move through the train. The color changes for each stage once you visit that stage. Certain stages require you to run that stage before you can go to the next stage.
View Menu
The View Menu provides access to a variety of functionality that you can use to customize the display of the tables in the user interface.
Embedded Help
Embedded help, which you access by clicking the Question Mark icon, provides additional information about the type of details required by certain fields.
Process Indicator
At the top of the user interface, in the right-hand corner, is a process indicator that you can use to monitor the status of a user action such as clicking Next to go to the next stage.
Search
In certain cases, you can customize your search, using advanced search capabilities to specify the search criteria.
Icons
The following icons are used in the user interface.
Table 1-1 Icons Used in the User Interface
| Icon | Icon Name | Icon Description |
|---|---|---|
| Add | Add a category for CDT or DT. Add a cluster in Advanced Clustering. Create new scenarios in Advanced Clustering. | |
| Approve Version | Approve a version of a CDT. | |
| Calculate | Initiate calculation of substitutable demand percentages in DT. | |
| Compare Two CDTs | Look at two CDTs side-by-side in the CDT Editor. | |
| Complete | Indicates the CDT is ready to be activated. | |
| Delete an entry in a table. Delete a scenario in Clustering. | Delete. | |
| Detach | Detach the table from the user interface for better viewing. |
Table 1-1 (Cont.) Icons Used in the User Interface
| Icon | Icon Name | Icon Description |
|---|---|---|
| Duplicate | Make a copy of a scenario. | |
| Edit | Edit the category attributes (CDT and DT) | |
| Embedded Help | Indicates that embedded help is available for the adjacent field. | |
| Execute | Execute the scenario. | |
| Export to Excel | Export the selected data to Excel. | |
| Go To Top | Adjusts table display. | |
| Go Up | Adjusts table display. | |
| No | Indicates No in Advanced Clustering. | |
| Query By Example | Provides access to a text entry field at the top of each column that you can use to search for data by an initial set of characters. | |
| Refresh | Update the table display or the stage status. | |
| Revert | Reverts the DT calculation. | |
| Save | Save the scenario. | |
| See Similarities | See similarities in DT Similarities Display. | |
| Set Version As Complete | Set a CDT version as complete. | |
| Select Date | Access a calendar in order to select a specific date. | |
| Show As Top | Adjusts table display. | |
| View One CDT | Access a CDT in the CDT Editor. | |
| Withdraw From Approved Version | Un-approve a version of a CDT. | |
| Yes | Acts as an indication that something exists in Advanced Clustering. |
Buttons
Buttons are used for navigation and to perform certain actions.
Table 1-2 Buttons
| Name | Description |
|---|---|
| Action | Provides access to Save, Approve, and Reject. |
| Advanced | Provides access to advanced search functionality. |
| Approve | Used to approve a CDT or a cluster. |
| Back | Used to navigate the process train. |
| Cancel | Cancels the action. |
| Complete | Used to make a CDT or DT model active. |
| Next | Moves to the next stage. |
| Reject | Used to navigate the process train. |
| Reset | Resets the values to the original ones. |
| Run | Initiates a run. |
| Search | Provides access to search functionality. |
| Stop | Stops a process. |
Browser Settings
The supported browsers include Mozilla Firefox 68+ ESR, Google Chrome (Desktop) 79+, and Microsoft Edge 44+.
Concurrent Browser Sessions
Users should not log into more than one browser session at the same time using the same username.
Localization
The default language for the application is English. If you are using a different language on your computer, you should adjust the language settings on your browser as appropriate.
Supported Characters for Text Entry
The following characters are valid for text input: all letters, all numbers, and the following characters: ’_’, ’#’, ’%’, ’*’, ’$’, ’ ’, ’,’ & ’-‘
Histograms
Certain stages have associated histograms that can help you analyze the data presented in that stage. You can adjust the way the histogram presents the data in two ways. You can select the number of bins that are used to display the data. In addition, you can select how the bins are defined: Equiwidth or Custom. Each of these options uses a specific algorithm to determine how the bins are defined.
The Equiwidth approach takes the minimum and maximum values in a set of numbers and divides that range into equally sized bins. For example, using the numbers from 1 to 100 with 10 bins, the histogram shows bins for 1-10, 11-20, and so on. If specific bins have no value represented (for example, if all the values are in the range of 1-10 and 91-100), then the histogram will not show that bin in the UI. Additionally, the histogram data series ranges are
shown using the actual minimum and maximum values for each of the bins. So rather than showing a range of 1-10, if the only value available was a 5, then the range for the first bin would appear as 5-5. In the Custom approach, each of the bins has an equal number of values represented, while the minimum and maximum number associated with the bin is adjusted. However, the bins are defined using distinct values instead of all the available values. The bins may or may not be of equal height, depending on how diverse the numbers are.
The two approaches differ in that the Custom approach only shows fewer bins than requested if there are fewer distinct values than what was requested for the number of bins.
The two approaches are similar in that both handle the Min/Max value display in a similar manner, using actual data values that are associated with the bin.
To determine which approach to use, you should consider what type of data you are trying to see and the amount of detail you want. For example, if you are trying to set a data filter value, and you want to do so using a common value, you may be able to see where most of the data falls using one of the algorithms, while the other algorithm may help you pinpoint a specific value within the range. The Equiwidth approach is negatively affected by values that are at the extreme ends of a value being binned. This can cause the majority of the data values to appear in a single bin. The Custom approach puts a greater emphasis on a value that is repeatedly found in a dataset. Depending on the values being charted, you may find that one of the approaches presents better data than the other approach.
In this guide
- Guide: AI Foundation User Guide
- Next: 10 Oracle Digital Assistant