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Table of contents
1.
Introduction
2.
Semi-Structured Data
2.1.
Semi-structured Data Characteristics
2.2.
Uses of Semi-structured data
3.
Problems faced in handling semi-structured data
3.1.
Possible solutions 
4.
Advantages of semi-structured data
5.
Disadvantages of semi-structured data
6.
Frequently Asked Questions
6.1.
What are the sources of semi-structured data?
6.2.
Can RDMS be used to store semi-structured data?
6.3.
Is JSON unstructured data?
6.4.
What are the types of big data?
7.
Conclusion
Last Updated: Mar 27, 2024
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Semi-structured Data

Author Prerna Tiwari
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Ashwin Goyal
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Introduction

In recent years, new and advanced data analysis techniques and software have emerged to enable everyone to gain significant business insights not only from quantitative or structured data in spreadsheets and statistics but also from qualitative or unstructured and semi-structured data in websites, emails, and customer service interactions, and other sources.

With techniques such as topic analysis and opinion mining, qualitative data analysis allows us to go beyond what has happened and discover why it happened. Analyzing semi-structured data can be pretty simple if the proper processes are in place.

Semi-Structured Data

Semi-structured data is information that cannot be organized in relational databases or does not have a strict structural framework but does have some structural properties or a loose organizational framework. Semi-structured data is a text that has been organized by subject or topic or that fits into a hierarchical programming language. Still, the text within is open-ended and lacks structure.

Emails, for example, are semi-structured by Recipient, Sender, Date, Subject, and so on, or are automatically classified into folders such as Outbox, Inbox, Spam, Promotions, and so on using machine learning.

Semi-structured Data Characteristics

Here are some characteristics of semi-structured data.

  • Semi-structured data is stored in the form of rows and columns as in databases.
     
  • Because the structure of semi-structured data is not well defined, computer programs cannot easily use it.
     
  • Semi-structured data is difficult to automate and manage because it lacks sufficient metadata.
     
  • The size and type of the same attributes in a group may differ.
     
  • Entities in the same group in semi-structured data may or may not have the same attributes or properties.
     
  • Similar entities are taken together and organized in a hierarchy in semi-structured data.

Uses of Semi-structured data

Some uses of semi-structured data are given below:

  • We can integrate data from various sources and exchange data between different systems using semi-structured data. Applications and systems must evolve, but this is impossible if we only work with structured data. 
     
  • Let's look at web forms. You may wish to modify forms and collect different information for different users. When using a traditional relational database, the database schema must be changed whenever a new field is required, and fields cannot be left empty. Semi-structured data allows you to capture any data in any structure without modifying the database schema or coding. Changing or removing data does not affect functionality or dependencies.
     
  • When working with semi-structured data, you get a flexible representation that does not require configuration or code changes as the data evolves. 
     
  • Data from various sources with varying notation and meaning can be collected and used. Relationships are described as references and are fully integrated into parent objects (tree). 
     
  • Semi-structured data allows for the preservation and support of complex query types of data structure and storage and the preservation of relationships between objects and complex schema. 
     
  • Queries and reporting can now be performed across multiple systems and data types.
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Problems faced in handling semi-structured data

While using semi-structured data, we face a lot of problems, some of which are mentioned below:

  • While semi-structured data increases flexibility, the lack of a fixed schema complicates storage and indexing. The schema and data are inextricably linked and interdependent, and a query can affect both. 
     
  • It is also difficult to run queries. OEM and XML formats aid in the storage and exchange of semi-structured data and help overcome some of these challenges.
     
  • As the volume of semi-structured data grows, new methods for managing, collating, integrating, storing, and analyzing it will emerge. 
     
  • Semi-structured data can assist us in capturing and processing data in its natural state rather than forcing it into an unnatural structure. Given the growing volume of this type of data, understanding the nature of semi-structured data and how to use it is critical.

Possible solutions 

  • Data can be stored in database management systems (DBMS) specifically designed to store semi-structured data.
     
  • XML is a popular format for the storage and exchange of semi-structured data. It enables the user to define tags and attributes for storing data in a hierarchical format.
     
  • In XML, the schema and the data are not inextricably linked.
     
  • Semi-structured data can be stored and exchanged using the Object Exchange Model (OEM). OEM organizes data in the form of a graph.
     
  • RDBMS can be used to store data by mapping it to a relational schema and then to a table.

Advantages of semi-structured data

  • The schema in semi-structured data is adaptable. It means that it is easily changeable.
     
  • Semi-structured data assists users who do not express their requirements in SQL.
     
  • In semi-structured data, dealing with heterogeneous sources is simplified.
     
  • Semi-structured data is constrained by a fixed schema.

Disadvantages of semi-structured data

  • Semi-structured data storage is difficult due to the lack of a fixed or rigid schema.
     
  • Semi-structured data queries are less efficient than structured data queries.

Frequently Asked Questions

What are the sources of semi-structured data?

Semi-structured Data Sources are:

  • Emails
  • XML and other markup languages are examples of markup languages.
  • Executables in binary form
  • Packets of TCP/IP
  • zip archives
  • Data integration from various sources
  • Websites
     

Can RDMS be used to store semi-structured data?

Yes, RDBMS can be used to store data. The data can be stored by mapping it to a relational schema and a table.
 

Is JSON unstructured data?

No, JSON is semi-structured data.
 

What are the types of big data?

The types of big data are Structured, Semi-structured, and Unstructured data.

Conclusion

In this article, we have extensively discussed the concepts of semi-structured data. We started by introducing semi-structured data, characteristics of semi-structured data, how to use semi-structured data, problems faced in handling semi-structured data, and possible solutions for semi-structured data usage, then concluded with the advantages and disadvantages of semi-structured data.

We hope that this blog has helped you enhance your knowledge regarding semi-structured data and if you would like to learn more, check out our article on unstructured data.

To study more about data types, refer to Abstract Data Types in C++ and Data Types in C++.

For peeps out there who want to learn more about Data Structures, Algorithms, Power programming, JavaScript, or any other upskilling, please refer to guided paths on Coding Ninjas Studio. Enroll in our courses, go for mock tests and solve problems available and interview puzzles. Also, you can put your attention towards interview stuff- interview experiences and an interview bundle for placement preparations. Do upvote our blog to help other ninjas grow.

Do upvote our blog to help other ninjas grow. Happy Coding!

 

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