Map configuration

Setting up the map

Follow the steps below to translate the desired business logic into the form of a Service Operations model:

Step	Description	Screenshot

Step	Description	Screenshot
1	Go to Service Operations administration. Click on the manage maps subsection and then on the create new model button. A new, blank model is created.
2	Expand the global settings menu on the right-hand side of the screen and set an appropriate name for the new model, for example, “E-commerce” and click on the apply changes button. Then, click on publish to save the configuration into the Service Operations database.
3	Create a new node for the map by clicking on the '+' button. Configure it as follows: Name: “User errors” Description: “Total number of errors per user” Icon: Iticon-open_search Value unit: “errors” Precision: 0 Best value: 0 Worst value: 20 Warning threshold: 15 Critical threshold: 18 Data availability timeout: 1 d No data criticality level: normal No data value: o No data text: “no data” Autodiscovery query: Copy and paste the LinQ query for number of errors, per user from the table above. Autodiscovery criteria: Select “clientIpAddress” from the dropdown menu. Click on apply changes, then click on publish. If no errors are raised, the new node should appear in the map.
4-7	Repeat the process to create 4 new nodes: Names: “application errors”, “traffic”, “conversion rate” and “avg. ticket”, respectively. Description: enter descriptive texts for each new entity. Value unit: “errors”, “visits”, “%” and “$”, respectively. Precision: 0,0,1,2, respectively. Best value: 0,3000,100,1000, respectively. Worst value: 500, 0, 0, 0, respectively Warning threshold: 400, 100, 1,10, respectively. Critical threshold: 450, 25, 0.5, 5, respectively. No data criticality: normal, critical, critical, critical, respectively. No data text: 0 Autodiscovery query: copy and paste the LinQ corresponding to each case from the table above. Autodiscovery criteria: applicationModule, none, none, none. Apply changes by the definition of each new metric and then publish.
8	Click on user errors and then on the clone button on the top buttons menu. A new icon called Duplicated user errors should be created. Repeat the process for application errors and traffic. Two more nodes are created as a result of these operations. Click on publish to save all changes.
9	Hover on the user errors node until a ‘+' shows at the node border. The click on the ‘+’ icon and, without releasing the click, drag the arrow to the border of the duplicated user errors entity. A link between the two nodes should be established. Repeat the process to link application errors with Duplicated application errors. Finally, link traffic, conversion rate and avg. ticket individually with Duplicated traffic. Publish to save all changes.
10	Change the name of the duplicated entities as follows: Duplicated user errors → Individual user status Duplicated application errors → App module status Duplicated traffic → Business status Publish to save all changes.	-
11	Click on both individual user status and app module status and clone each of them. Then, link individual user status and app module status with their respective new, cloned entities. Rename the duplicated entities to "UX status" and "app status", respectively. Clone UX status and rename the new node to "e-commerce status". Create links from UX status, app status and business status to e-commerce status. Publish to save all changes.
12	Click on the link between user errors and individual user status. In the menu that appears on the right-hand side of the screen, set both from column to column with clientIpAddress. Click on apply to keep this configuration. Repeat the previous procedure with application errors and app module status.
13	Click on the user errors node. In the entity definition form, click on the metadata tab. In there, select its type as KPI, and subtype as operational. Apply to keep the changes. Repeat the same process to for all leaf nodes in the tree, such as application errors, traffic, conversion rate and avg. ticket. The subtype of the last three can be set as "business" as that is the type of metric measured in their case. Note that entities of "KPI" type are displayed as squares in the map. Publish to save all changes.
14	Click one-by-one on all non-KPI entities in the map and, in the entity definition form, configure the following for them: Auto-mode: enabled. Apply all individual changes and publish to save.	-
15	Finally, click on the map global properties menu to show all options relative to the e-commerce model and set time range > amount and unit to ‘1 d’ (one day). This setting specifies the rolling time window the application will consider for all data analysis processes.

Once all these steps are finished, the model will be available for its first execution. To do that, navigate to the available models section in the administration main menu and verify that "e-commerce" appears in the list of available models. Click on the play button to launch the associated jobs that will start calculating the necessary data to populate the more. Do not select the stats or incidents module for the time being.

Select the default sampling period (60 seconds) and click on the OK button to initiate the calculations:

After a few moments, the calculations engine will process the first batch of data and inject the resulting calculations in the results table. Access to the topology view under Services overview should provide an initial representation and analysis of the map as follows: