Search: output-data-table

. Output Output Record: The record created from the Data Service, stored in an Entity type object

an existing record for the selected Entity from the Data Service. In the body of activity Entity: Select the entity you want to use with the activity. The property only supports entities imported from Data Service

Data Service Activities

and arguments will be logged at Verbose level. * **Output** ** Output Record: The record created from the Data

Entity from the Data Service. == **In the body of activity** == * **Entity: **Select the entity you want to use with the activity. The property only supports entities imported from Data Service

will be logged at Verbose level. Output Out Data Service Client: The DataService Scope activities following

RCA.Activities.DataService.DataServiceScope Description This scope helps to run Data Service activities Properties Input Authentication Type: Determines the method used to authenticate with the Data

[1] Data Service Scope

Data Service Activities

" %) ((( RCA.Activities.DataService.DataServiceScope == **Description** == (% class="wikigeneratedid" %) This scope helps to run Data Service

to authenticate with the Data Service. There are two options available: Agent and ManagedToken ** **Data Service Client: **Re-use token of previous Data Service Scope * **Manage Token**:  ** **Authentication Token

. [Workflow] Duplicated output workflow parameters. 2. [Task] Can't stop running task. 3. [Info] Can't change password. 4. [Queue] Agents are not shown in the transaction table. 5. [Productivity] Can

correct data according to chosen Agent Group and Agent. 13. [Messages] Improve messages and notifications

in akaBot Center, check out the release notes below. == **Bug fixes** == ~1. [Workflow] Duplicated output

] Agents are not shown in the transaction table. 5. [Productivity] Can not create a new schedule

events in Subscribed Events. 12. [Report] Does not export correct data according to chosen Agent Group

in edit mode. Select Data Source. Search jhi_dataset table and drag it to the data source interface. Select a relationship with an existing table on the data source interface.

Dataset The default columns in the Business Dataset table are Manual Time and Hourly Cost. You can also

[1] ROI data set

in edit mode. 1. Select **Data Source**. 1. Search **jhi_dataset **table and drag it to the data source interface. 1. Select a relationship with an existing table on the data source interface. ))) {{velocity

** == The default columns in the **Business Dataset** table are **Manual Time** and **Hourly Cost**. You can also

to a pipeline that has Automation Type is Confident, then export them and change the status. Table

. (% style="text-align:center" %) [[image:image-20220420200751-2.png||cursorshover="true" data-xwiki-image

||cursorshover="true"]] ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table of Content

. Adding a new table In order to create a new table, click [Add Field], select Data Type = “table

A basic element in the extraction schema is the data field. However, akaBot Vision enables the capture of even more complex structures like tables. Adding a predefined table field If you are missing

[4] Capture Custom Table Data

Capturing Custom Table Data in akaBot Vision

Customizing Data Extract

, click [Add Field], select Data Type = “table” and then input the value you want to create a new table

="wikigeneratedid" id="HParagraph1" %) A basic element in the extraction schema is the data field. However, akaBot Vision enables the capture of even more complex structures like tables. == **Adding a predefined table

Each Pipeline defines the structure of Data fields that akaBot Vision extracts. Description When editing this structure you have two options: Use pre-trained Data fields – AkaBot Vision’s Generic AI engine has been pre-trained to recognize specific Data fields and enables you to start extracting data

[3] Configure Fields for Data Extraction

Customizing Data Extract

Configuring Fields for Data Extraction

="wikigeneratedid" id="HParagraph1" %) Each Pipeline defines the structure of Data fields that akaBot Vision

options: * Use pre-trained Data fields – AkaBot Vision’s Generic AI engine has been pre-trained to recognize specific Data fields and enables you to start extracting data without any additional training

: Choose Automation Type and set conditions for required fields and data formats The Automation Type

will be bypassed Bypass wrong data formats: All the documents with wrong data formats inside will be moved

. If you turn this mode on, all the wrong data formats will be bypassed Step 3: Click [Save] to save

Customizing Data Extract

Automation Type and set conditions for required fields and data formats ))) * The Automation Type will have

fields will be bypassed * Bypass wrong data formats: All the documents with wrong data formats inside

these later. If you turn this mode on, all the wrong data formats will be bypassed [[image:image

on: Built-in checks – we perform Data Integrity checks based on values found on the document. Such checks

the Extraction schema you should be seeing the message for the required fields with no captured value. Table

Customizing Data Extract

– we perform Data Integrity checks based on values found on the document. Such checks could

value. ))) ))) ))) (% class="akb-toc" %) ((( (% class="akb-toc-title" %) ((( Table of Content

Automation of Fields Capture Custom Table Data Configure Automation Type for Pipeline Configure Fields for Data Extraction

[6] Customize Data Extract

Customizing Data Extract

* [[Automation of Fields>>path:/bin/view/akaBot%20Vision/Customizing%20Data%20Extract/Automation%20of%20Fields%20in%20akaBot%20Vision/]] * [[Capture Custom Table Data>>path:/bin/view/akaBot%20Vision

/Configure%20Automation%20Type%20for%20Pipeline%20/]] * [[Configure Fields for Data Extraction>>path:/bin