Before the cloud, most data was organized and neatly tucked away in databases or spreadsheets. Organizations now have access to a considerably broader range of data in a variety of forms. Semi-structured data created by IoT devices, mobile applications, and web pages have great value if firms can successfully mine it. This article delves into what semi-structured data is, the issues that come with evaluating it, and the tools organizations use to maximize its value.

What is Semi-Structured Data?

Unlike relational databases or other types of data tables, semi-structured data does not adhere to the tabular structure of structured data. Nevertheless, it includes tags or markers to segregate semantic pieces and impose hierarchies of records and fields within the data. As a result, it is also known as a self-descriptive structure.

Entities of the same class may have various features while being grouped close to each other in semi-structured data, and the order of the attributes is immaterial.

Semi-structured data has grown more frequent with the advent of the internet, as full-text texts and databases are no longer the only forms of data. Many applications require a medium for information sharing, and semi-structured data is frequent in object-oriented databases. Emails, for example, are semi-structured by Recipient, Subject, Date, Sender, and so on, or are automatically categorized into categories such as Inbox, Spam, and Promotions using machine learning.

Semi-structured data is a cross between images and videos. It may contain Meta tags referring to the place, date, or person who took them, for example, but the information contained inside them is unstructured. Consider Facebook, which organizes information by Marketplace, Users, Groups, Friends, and so on, but the comments and content inside these groupings are disorganized.

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Characteristics of Semi-Structured Data

  1. Data does not correspond to a data model, yet it has some structure.
  2. Data cannot be kept in rows and columns, as in databases.
  3. Semi-structured data includes tags and components (metadata) that organize data and specify how it is kept.
  4. A hierarchy is formed by grouping similar elements together.
  5. The qualities or properties of entities in the same group may or may not be the same.
  6. Lacks adequate metadata, making automation and data management problems.
  7. The size and type of the same properties may differ in a group.
  8. Computer programs cannot easily utilize it due to the lack of a well-defined structure.

Types of Semi-Structured Data

Images/Videos

When you snap a picture with your phone, the image is saved in the gallery along with the timestamp, date, and metadata. After that, you may rename the image or organize it into a new group.

Emails

Emails contain structured information about the sender, receiver, subject, and date, and are automatically categorized as Inbox, Spam, or Outbox. The material in the emails is unstructured and searchable using keywords.

Social Media Platforms

Facebook groups, pages, and Marketplaces arrange data, but comments, content, and likes are semi-structured. Tweets on Twitter and images/videos on Instagram, Pinterest, and YouTube are semi-structured data.

Machine Generated Semi-Structured Data

Semi-structured data includes weather updates, predictions, traffic situations, satellite photos, and video recordings.

Semi-Structured Data Examples

  • Email
  • NoSQL databases
  • CSV, XML, and JSON documents
  • Electronic data interchange (EDI)
  • HTML
  • RDF

Semi-Structured Data Sources

Semi-structured data is produced by various sources, including many common consumer gadgets. This data format is getting more popular and provides a huge opportunity for enterprises. The emergence of strong cloud platforms has made it feasible to quickly store, analyze, and analyze semi-structured data, uncovering previously unattainable insights. Here are a handful of examples of semi-structured data sources that demonstrate the utility of this sort of data.

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From IoT Sensors

IoT sensors generate data in a variety of forms, including semi-structured data. These remote sensors have a wide range of applications and may provide large volumes of usable data. Manufacturers, for example, utilize data from equipment-mounted sensors to monitor vibration levels, heat, and output to estimate when gear will need maintenance. An a. IoT devices offer a wide range of uses in healthcare, allowing clinicians to monitor critical indicators for high-risk patients by obtaining data from wearable monitoring devices. This information may be gathered and analyzed to determine patient adherence to treatment programs and to track medically important information such as blood sugar levels over time.

Data From The Internet

The enormous increase in semi-structured data is also due to the expansion of the web. Semi-structured markup languages include HTML, XML, and other markup languages. Their schemas might be descriptive, incomplete, or changing. Lists and tables are frequently mixed with unstructured text in semi-structured online data. This data can be mined in ways that unstructured data, such as plain text, cannot. Email is frequently the same, containing unstructured text and structured data such as sender, recipient, time and date, and so on. Given the massive amount of internet material and data created daily, the capacity to evaluate these rich data sources necessitates using current data analytics technologies.

Advantages of Semi-Structured Data

  • A set schema does not bind it.
  • It is adaptable since the schema is readily altered.
  • Data is transportable.
  • It assists customers who are unable to articulate their requirements in SQL. It can easily cope with a variety of sources.

Disadvantages of Semi-Structured Data

  • The lack of a stable, rigorous format makes data storage challenges.
  • Interpreting the link between data is challenging as there is no separation between the schema and the data.
  • Queries are less efficient than structured data.

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Semi-Structured Data Storage

Data can be stored in DBMS created specifically for semi-structured data. XML is commonly used for semi-structured data storage and communication. Its user may specify tags and attributes to store data in a hierarchical format. In XML, the schema and data are not tightly connected. Storage and exchange of semi-structured data can be accomplished through the Object Exchange Model (OEM). It organizes data in the shape of a graph. RDBMS may store data by mapping it to a relational schema and then to a table.

Semi-Structured Data Extraction

The extraction steps of implication rules are supplied following the extraction of semi-structured data implication rules. It consists mostly of three steps: data collection and processing, data computation, and rule extraction.

The data collection and processing step primarily extracts semi-structured data from websites before converting and preprocessing it into structured data that may be used for rule extraction. The source data of a web page is semi-structured, and the source data include missing data, noisy data, and inconsistent format data. Semi-structured data on web pages are retrieved using a data extraction tool. Then, preprocessing procedures on missing data, noisy data, and format data in source data are performed before being transformed into structured data. Structured data may be in XML format or a relational database. 

During the data calculations step, the minimum support and strength of implication are computed and the threshold support and strength are supplied so that implication rules can be extracted during the extraction stage. The extraction rules are extracted using the implication connection of the minimal support and the strength of implication of threshold in the last step of implication rule extraction. Finally, the findings are visualized.

Conclusion

Managing, collating, integrating, storing, and analyzing semi-structured data will evolve as the volume of semi-structured data grows. A lot of people. Given the expanding volume of this type of data, understanding the nature of semi-structured data and how to use it is critical. 

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FAQs

1. What is a semi-structured data example?

Semi-structured data sources include emails, XML and other markup languages, TCP/IP packets, binary executables, zipped files, data integrated from different sources, and web pages.

2. What is the meaning of semi-structured data?

Semi-structured data refers to data that is not recorded or formatted in the usual ways.

3. What is the meaning of semi-structured?

The semi-structured data model differs from tabular data models and relational databases due to the lack of a set schema. The data, however, is not fully raw or unstructured; it does contain certain structural features, such as tags and organizational information, which make it easier to examine.

4. Is CSV semi-structured data?

Yes, CSV is semi-structured data.

5. What is an example of unstructured data?

Rich media is one example of unstructured data. Data from media and entertainment, surveillance, geospatial data, audio, and weather. Collections of documents.

6. What is the difference between unstructured and semi-structured data?

The organizational level determines the distinction between semi-structured and unstructured material. While the latter is available in many forms and types, the former is structured using tags and structures.

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