As an example, you want to create a model for a CSV input file that contains the following content:

first,last,street,city,state,zip
Carrine,Stone,17 Torrence Street,Livingston,PA,10173
Poona,Tillkup,52 Perez Avenue,Livingston,PA,10256
Tasha,Herrera,158 Shiraz Boulevard,Kensington,WA,33823
John,Washington,22A Zangville Drive,Tucson,AZ,20198
Jane Hochuli 4483 Central Street Suite 30 Phoenix PA 38721

The following image shows the structure that

Intelligent Structure Discovery

discovers based on the input file:

You can see that

Intelligent Structure Discovery

created nodes representing the fields in the input file, such as

first

,

last

,

street

,

city

,

state

, and

zip

.

The structure represents not just the data fields themselves, but also the relationship of the fields to each other. For example,

Intelligent Structure Discovery

recognized that the data

Carrine,Stone

represents the first name and last name of a person. The nodes

first

and

last

are grouped together under the node

fullName

, representing the relationship of the data with each other.

Intelligent Structure Discovery

also recognized that the data as a whole represented addresses. The data is grouped under a parent node

address

.

The nodes represent fields that are part of the output. Nodes that are related are grouped into an output group. Output groups can contain one or more nodes.