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Process Mining

Last updated Dec 18, 2024

TemplateOne-MultiFiles Input Fields

Introduction

If your data for cases and events is split up into different input files, you use the TemplateOne-MultiFiles 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.

For each table, this must be a tsv (tab-separated) file or .csv (comma-separated) file that contains a column for each input field.

This section contains an overview of the input tables for TemplateOne-MultiFiles. 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.

Important:

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 tables below and that the file names match the table names.

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 of the process app.

Note: When a field is mandatory, it should not contain NULL values.

Field types

Below is an overview of 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

Cases_raw

The Cases_raw table contains the detailed information for the status, type, value and description for the related case.

Name

Data type

Mandatory Y/N

Description

Case_ID

text

Y

The unique identifier of the case the event belongs to.

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.

Custom Case Fields

Additionally, 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.

Custom fields have generic names. Below is an overview of 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}

integer

N

Note:

When you use a custom field, you should rename the field in using Data manager to a name that matches the value. See Fields.

Custom fields that are empty in your dataset, are not displayed in Process Mining.

Event_log_raw

The Event_log 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.

Event_ID

integer

Y*

The unique identifier of the event.

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.

*) Event_ID is optional, but is mandatory if you want to use Due dates.

**) 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 Event Fields

Additionally, 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. Below is an overview of the custom fields that can be used in a process app.

Name

Data type

Mandatory Y/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

Note:

When you use a custom field, you should rename the field in using Data manager to a name that matches the value. See Fields.

Custom fields that are empty in your dataset, are not displayed in Process Mining.

Tags_raw

The Tags_raw table contains fields for tags that are defined in your process. If you upload tags, the Tags dashboard will be enabled.
Note:

Tags_raw is an optional table.

Name

Data type

Mandatory Y/N

Description

Case_ID

text

Y

The unique identifier of the case the tag belongs to.

Tag

text

Y

A user-friendly name to identify the tag.

Tag_type

text

N

The category to which the tag belongs to.

Due_dates_raw

The Due_dates_raw table contains fields for due dates that are defined in your process. If you upload due dates, the Due dates dashboard will be enabled.
Note:

Due_dates_raw is an optional table.

Name

Data type

Mandatory Y/N

Description

Event_ID

integer

Y

The unique identifier of the event the due date belongs to.

Due_date

text

Y

The name of the due date.

Actual_date

datetime

Y

The actual moment the event associated with the due date happened

Expected_date

datetime

Y

The moment the event associated with the due date was expected to happen.

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