When you provide a sample file,

Intelligent Structure Discovery

determines the underlying structure of the information and creates a model of the structure. After you create an

Intelligent structure model

you can view, edit, and refine it. For example, you can select to exclude or combine structure elements. You can normalize repeating groups.

When you finish refining the model, you can export it and then associate it with a data object in a Big Data Management mapping.

The following image shows the process by which Intelligent Structure Discovery deciphers the underlying patterns of data and creates a model of the data patterns.

You can create models for semi-structured data from Microsoft Excel, Microsoft Word tables, PDF forms, and CSV files, or unstructured text files. You can also create models for structured data such as XML and JSON files.

You can quickly model data for files whose structure is very hard, time consuming, and costly to find, such as log files, clickstreams, customer web access, error text files, or other internet, sensor, or device data that does not follow industry standards.