- Release notes
- Before you begin
- Getting started
- Integrations
- Managing access
- Working with process apps
- Creating apps
- Loading data
- Uploading data
- Retrieving the SQL Server database parameters
- Setting up a SQL Server account for data upload using an extractor
- Loading data using Theobald Xtract Universal
- Customizing process apps
- Data transformations
- TemplateOne app template
- Purchase to Pay app template
- Order to Cash app template
- Basic troubleshooting guide
Introduction
If your data for cases and events is available in one input file, you use the TemplateOne-SingleFile app template.
If you want to create a new TemplateOne process app, you must upload a dataset that contains the data to be used in the TemplateOne.
This must be a tsv (tab-separated) file or .csv (comma-separated) file that contains a column for each input field.
Table names and field names are case-sensitive. Always make sure that the field names (column headers) in your dataset match the field names (in English) in the table below and that the file name is Event_log_raw.
Tutorial
When creating your first dataset, check this video below for a tutorial. Use this to better understand how to create a dataset and use it in a TemplateOne process app.
Mandatory and optional Fields
Your input data file can have different fields or different field names. However, some fields are mandatory, which means that the data must be available in your input data file for a correct working the process app.
When a field is mandatory, it should not contain NULL values.
Field types
The following table describes the different field types and their default format settings.
| Field type | Format |
|---|---|
| boolean | true, false, 1, 0 |
| datetime | yyyy-mm-dd hh:mm:ss[.ms], where [.ms] is optional.Refer to the official Microsoft documentation if you want to change the date format. |
| double | Decimal separator: . (dot)Thousand separator: none |
| text | N/A |
| integer | Thousand separator: none |
The following table describes the input fields. For each field, the name, the data type, and a short description are displayed. Apart from that, it is indicated whether the field is mandatory.
Event_log_raw
The Event_log_raw table contains information on the activities executed in the process.
| Name | Data type | Mandatory Y/N | Description |
|---|---|---|---|
Activity | text | Y | The name of the event. This describes the step in the process. |
Case_ID | text | Y | The unique identifier of the case the event belongs to. |
Event_end | datetime | Y | The timestamp associated with the end of executing the event. |
Case | text | N | A user-friendly name to identify the case. |
Case_status | text | N | The status of the case in the process. For example, open, closed, pending, approved, etc. |
Case_type | text | N | The categorization of the cases. |
Case_value | double | N | A monetary value related to the case. |
Automated* | boolean | N | Indicates whether the event is manually executed or automated. |
Event_cost | double | N | The costs for executing the event. |
Event_detail | text | N | Information related to the event. |
Event_start | datetime | N | The timestamp associated with the start of executing the event. |
Team | text | N | The team that executed the event. |
User | text | N | The user who executed the event. |
*) Automated is part of the input data. However, if you need to derive whether an event is automated or not, you need to customize this in the data transformations to create that logic based on the required information. It is recommended to do this in the Event_log.sql file. Locate the statement that contains ...as "Automated" and replace it, for example, with a statement as displayed below.
case when Event_log_base."User" = 'A' then pm_utils.to_boolean('true') else pm_utils.to_boolean('false') end as "Automated"
case when Event_log_base."User" = 'A' then pm_utils.to_boolean('true') else pm_utils.to_boolean('false') end as "Automated"
Custom Fields
The following custom case fields are available.
- 30 fields of type text;
- 10 fields of type double;
- 10 fields of type datetime;
- 10 fields of type boolean;
- 10 fields of type duration.
The following custom event fields are available.
- 30 fields of type text;
- 10 fields of type double;
- 10 fields of type datetime;
- 10 fields of type boolean;
- 5 fields of type duration.
Custom fields have generic names.The following table describes the custom fields that can be used in a process app.
| Name | Data type | Mandatory Y/N |
|---|---|---|
| custom_case_text_{1...30} | text | N |
| custom_case_number_{1...10} | double | N |
| custom_case_datetime_{1...10} | datetime | N |
| custom_case_boolean_{1...10} | boolean | N |
| custom_case_duration_{1...10} | duration | N |
| custom_event_text_{1...30} | text | N |
| custom_event_number_{1...10} | double | N |
| custom_event_datetime_{1...10} | datetime | N |
| custom_event_boolean_{1...10} | boolean | N |
| custom_event_duration_{1...5} | integer | N |
When you use a custom field, you should rename the field in using Data manager to a name that matches the value. Check out Fields.
Custom fields that are empty in your dataset, are not displayed in Process Mining.