Internally, Corticon accesses all data sources using SQL. A Corticon Vocabulary is fundamentally relational in nature, and conceptually equivalent to the elements of a typical relational database:

Corticon Vocabulary Relational Database
Vocabulary Schema
Vocabulary: Entity Table
Vocabulary: Attribute Table Column or Field
Vocabulary: Association Relationship between Tables
Ruletest Output Table Row(s) or Record(s)

How REST can conform to a relational database schema

Corticon uses the Progress® DataDirect® Autonomous Rest Connector to access REST Datasources, which maps the returned JSON to a relational database schema and translates SQL statements to REST API requests. When configuring a REST Datasource you can either have the Autonomous Rest Connector discover the schema for a REST service or provide your own schema file. The schema tells the Autonomous Rest Connector how to map JSON in memory database tables which Corticon will then access with SQL.

JSON data is hierarchical, not relational, but can be mapped to a relational model. When using schema discovery, Autonomous Rest Connector will determine how to perform this mapping. If you need finer control over the mapping, you can provide your own schema file.

The Autonomous Rest Connector handles the complexities of mapping JSON to a relational representation but you need to understand the rules applied to perform the mapping. This is best done by example. Imagine you have a REST service which returned the JSON. 
{
    "applicant": [
        {
            "name": "Sydney Smith",
            "income": 57000,
            "address": {
                "street": "101 Main Street",
                "city": "Raleigh",
                "state": "NC"
            },
            "children": [
                {
                    "name": "Robert Smith",
                    "dob": "2017-04-19"
                },
                {
                    "name": "Chelsea Smith",
                    "dob": "2014-11-07"
                }
            ]
        }
    ]
}
Looking at this, you can see there is one applicant, Sydney Smith, with two children and other information about Sydney. The Autonomous REST Connector would represent this relationally as two tables, "applicant" and "children". The applicant table would contain columns for:
  • name
  • income
  • street
  • city
  • state
The "children" table would contain columns for
  • name
  • dob
When mapping the JSON to in memory tables, the Autonomous Rest Connector would map Sydney to the "applicant" table and her two children to the "children" table. The Autonomous Rest Connector would also define a primary key/foreign key relationship between the tables so that the children for an applicant can be identified.

In this simple example:

  • The applicant and their attributes were added to the applicant table
  • The nested address data was "flattened" and made attributes of the applicant
  • The nested array of children objects were mapped to an associated table
Corticon Vocabulary Relational Database REST mapping
Vocabulary Schema Schema (even if implicit)
Vocabulary: Entity Table Object
Vocabulary: Attribute Table Column or Field Number, string, or null
Vocabulary: Association Relationship between Tables Array of objects, strings, or numbers
Ruletest Output Table Row(s) or Record(s) Object Instances

For an example a schema in a REST Datasource, see Mapping REST Service metadata .