Grooper Help - Version 25.0
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Table Instance

Field Container Instance Grooper.Core

Represents an instance of a Data Table object on a document.

Remarks

The Table Instance class models a single occurrence of a Data Table within a document’s extracted data.
It serves as the container for all Table Row Instances in a table, enabling Grooper to capture, validate, and present tabular data such as line items, transactions, or repeating lists.

Role in Grooper

Table Instances are created automatically during extraction as children of a Document Instance or Section Instance.
The Table Extract Method assigned to a Data Table determines how the table is recognized and populated.

Table Instances are essential for representing structured, repeating data in documents. They support both simple and complex tables, including those with dynamic columns, calculated footers, and context-based extraction.

Usage and Configuration

  • Table Instances are managed by Grooper’s extraction engine and are not created or configured directly by end users.
  • They are visible in the Data Review UI, where users can review, validate, and edit extracted table data.
  • Advanced users may interact with Table Instances via expressions, custom code, or API integrations for automation, validation, or custom export scenarios.

Extraction, Validation, and Review

  • Table Instances are created and populated during the Extract activity, using the extraction logic defined in the Data Table.
  • Validation is performed automatically or on demand, using rules defined in the Data Table, Data Model, and individual Data Columns.
  • The Table Instance tracks validation status, error messages, and change tracking for all contained rows and cells.
  • In the Data Review UI, the Table Instance provides a grid view for reviewing, editing, and confirming tabular data.

Hierarchy and Structure

Integration with Other Grooper Features

  • Table Instances support advanced features such as dynamic column ordering, calculated footers, and context-based extraction.
  • They are used in workflows for data extraction, validation, review, and export, ensuring that tabular data is processed accurately and efficiently.
  • The class provides utility methods for annotation, error handling, and value management, supporting robust quality control and exception handling.

Diagnostics and Audit Trails

  • Table Instances participate in diagnostic logging and audit trails for extraction, validation, and review activities.
  • Diagnostic artifacts may include validation error messages, change logs, and audit trails of user edits or automated changes.

Properties

NameTypeDescription
General
Additional Data
Document Reference

See Also

Notification