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Semi-Structured Data Model in DBMS

The relational model has evolved into the semi-structured model. In this model, we can’t tell the difference between data and schema. This model can primarily be used to represent data from some data sources that aren’t bound or constrained by the schema.

In this article, we will dive deeper into the Semi-Structured Data Model in DBMS according to the GATE Syllabus for (Computer Science Engineering) CSE. Keep reading ahead to learn more.

Table of Contents

What is the Semi-Structured Data Model in DBMS?

This model is a DB (database) model in which the data and the schema are not separated, and the amount of structure employed is determined by the goal or the purpose.

What is Semi-Structured Data in DBMS?

Semi-structured data refers to the structured data that doesn’t adhere to the tabular structure of the data models that are associated with relational DBs or any other types of data tables. It includes tags or any other markers in order to segregate semantic pieces and enforce hierarchies of fields and records within the data. As a result, it’s known as a self-descriptive structure.

Despite being grouped close to one other, entities of the same class in semi-structured data may have diverse characteristics, and the order of the attributes is irrelevant.

Because full-length texts, documents, and DB are no longer the sole types of data, semi-structured data has become more common since the internet’s inception. Semi-structured data is ubiquitous in object-oriented DBs, and many applications require a means or a medium for information transmission.

Example

A common example of this type of data model is web-based data sources in which the data and the schema of the website are indistinguishable. Some entities may be missing attributes, while another one may have an extra attribute in this model. This approach allows for data storage flexibility. It also allows the qualities to be more flexible. Any value that we store in any attribute can be either an atomic value or a collection of data.

Emails, HTML, web pages, etc., are a few more examples of this type of data model.

Pros of Semi-Structured Data Model in DBMS

The following are some of the benefits of this model:

  • It can be used to represent data from some data sources that aren’t bound by a schema.
  • It offers a versatile format for data sharing between various DBs.
  • Viewing structured data as semi-structured can be beneficial for browsing purposes.
  • It is simple to alter the schema.
  • It’s possible that the data transfer format is portable.

Cons of Semi-Structured Data Model in DBMS

The main disadvantage of using a semi-structured data model is that queries cannot be performed as quickly as they can in a more limited structure, such as the relational model. In a semi-structured DB, records are typically stored with unique IDs referenced with pointers to their disc location. It makes navigational or path-based queries very efficient, but it is inefficient for searching multiple records (as is common in SQL) because it must seek around the disc following pointers.

One standard for expressing semi-structured data is the Object Exchange Model (OEM), while another is XML.

Keep learning and stay tuned to get the latest updates on the GATE Exam along with Eligibility Criteria, GATE Syllabus for CSE (Computer Science Engineering), GATE CSE Notes, GATE CSE Question Paper, and more.

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