A dataset is a structured collection of data in the form of documents, videos, images, or other types of files. It is different from a database, which is a collection of data stored as multiple datasets.
In statistics, datasets are typically stored in tabular form, making it easier for users to organize and process the information visually. In information technology, datasets are stored electronically, making it easy to access, manipulate, and update through a computer program.
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Types of datasets in information technology
File-Based Datasets
This type consists of a dataset stored in a single file, such as an AutoCAD DXF file, in which each DXF file is a dataset. In file-based datasets, each dataset is assigned to a category. For example, in an AutoCAD file, each dataset stores data from different AutoCAD layers.
Folder-Based Dataset
In this type, the dataset is located with the folder holding the data. A computer CSV file is an example of a folder-based dataset.
Database Datasets
A database dataset is a set of structured data stored in a database. For example, the resources database in Oracle consists of tables listing information such as vehicles, users, and equipment. The resources are that dataset, while the vehicle, users, and equipment are the database.
Web Datasets
When a dataset is stored on an internet file, it is called a web dataset. For example, the Web Feature Service server is a web dataset.
How is a dataset used?
In information technology, a dataset can be used through various computer applications depending on the type of data. For example, a dataset can hold information about health insurance records or medical records, which can be accessed by a program running on the system. A dataset is also used for operating system data itself such as macro libraries, system variables, or source programs.
IT Business Edge takes a closer look at some of the top tools for working with large datasets.
Dataset limitations
While datasets are powerful and extremely useful in a variety of applications, they do have some limitations. If there is an error in a dataset, it does not have an in-built system to pinpoint the error. A single error in the data can result in the corruption of the entire dataset. Complex error detection techniques might need to be applied to find and fix the error.
