Everything You Need to Know about Semi-Structured Data with Semi-Structured Data Examples
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Data was usually stored in spreadsheets or databases in a neat and organized way. Data has become diverse after the advent of the cloud, mobile apps, web pages, and IoT devices. Such data, when mined effectively, can prove to be highly effective for businesses.
Big data comprises a high volume and huge variety of data. There are three types of Big Data i.e. structured, semi-structured, and unstructured data.
Semi-structured data refers to the kind of data that does not follow a rigid or fixed tabular structure and is not stored in conventional data models. Semi-structured data lies in the middle of structured and unstructured data.
Structured data is quantifiable and can be understood by both human beings and machines. Unstructured data, on the other hand, comprises non-numerical data which computers cannot understand.
What Is Semi-Structured Data?
Semi-structured data, also known as partially-structured data, is not found in a relational database. However, the data has some structure due to the presence of metadata, semantic elements, and organizational properties that allow us to analyze it.
Metadata is a small portion of a file that contains all the information such as data creation, time, file size, length, sender/recipient data, and much more. Semi-structured data can be searched or analyzed with its metadata.
What Are The Characteristics Of Semi-Structured Data?
Some of the main characteristics of semi-structured data are:
Data is not stored in a database model but still has some structure. Semi-structured data cannot be stored as rows and columns in the database.
The data is grouped by tags and elements (Metadata). Semi-structured data is difficult to manage as it comprises insufficient metadata. The data contains insufficient metadata, which renders automation difficult.
The entities may vary in attributes and properties within the same group. However, the attributes may differ in terms of size and type.
Similar entities of data are grouped together.
Semi-structured data lacks hierarchy, making it difficult for computer programs to use.
What Are The Sources Of Semi-Structured Data?
Some of the sources of semi-structured data are:
XML (Extensible Markup Language)
XML is used to sort data in a hierarchical form. XML is a markup language that was created by World Wide Web Consortium and is available as open-source software. It makes the data readable by both human beings and machines.
XML allows us to create custom self-descriptive tags or language that match the application. Some of the applications of XML are:
XML helps simplify the creation of HTML documents for large websites. XML helps to exchange information between websites and systems.
The best aspect of XML is that any type of data can be expressed through it.
HTML code (Hypertext Markup Language)
Markup Language or HTML is a standard markup language that is similar to XML. However, it displays data on a web browser as compared to XML, which only transmits the data.
HTML is used by programmers to create web pages and displays images or text on the screen with the help of HTML elements.
The data within the images is unstructured. The web browser first receives the HTML documents from a web server and then converts them into displayable web pages. HTML helps to define and organize the data and make it readable by the users.
SGML (Standard Generalized Markup Language)
SGML is an international standard for defining markup languages that are derived from Generalized Markup Languages (GML) SGML was developed by International Organization for Standards (ISO) in 1986. SGML basically allows users to work on standardized formats. HTML is an application of SGML.
CSV (Comma-separated values)
Comma Separated Values or CSV is a text file that contains data separated by commas. CSV is used by spreadsheet programs such as Excel. Each new line in CSV represents a new database row, and each row contains one or more values separated by commas.
CSV helps transfer data present in XLSX files to other programs that do not support such formats. For instance, you can transfer the. XLSX data to a CSV file and then upload it onto an online software. You can also import contacts into a CSV file and then open it on another email platform. CSV is supported by many platforms such as Microsoft Excel, Apple Numbers, Google Sheets, Notepad, etc.
Avro is a data serialization network created by Avro Apache for its Apache Hadoop Project. Avro uses JSON format to organize and serialize the data in a binary format. Avro uses two types of schema to structure the data.
One is made for human editing, known as Avro IDL, and the other is made for machine editing based on JSON. AVRO uses JSON for defining data types and protocols and serializes data in a compact binary format.
ORC (Optimized Row Columnar)
Optimized Row Columnar (ORC) file format is used to store Hive data efficiently. It is more advanced than other Hive file formats and improves performance when Hive is reading, storing, or transferring data.
Transmission Control Protocol (TCP) is a communications standard that allows computer programs and software to receive and send messages across a network. It is specifically designed to send packets and ensure smooth and reliable delivery of messages and data.
Data integration from different sources
What Are The Multiple Advantages And Disadvantages Of Using Semi-Structured Data?
The advantages and disadvantages of semi-structured data are:
The semi-structured data is not limited to the rigid database.
The data is highly flexible as the schema can be changed.
Semi-structured data supports users who cannot use SQL.
Semi-structured data can be viewed as structured data.
Semi-structured data can easily deal with the heterogeneity of sources.
Semi-structured can evolve over time as more and more attributes are added to it.
Semi-structured lacks structure making it difficult to store data.
Data lacks schema, so it becomes difficult to interpret the relationships between the data.
Queries in semi-structured data are less efficient as compared to structured data.
What Are The Problems Faced In Storing Semi-Structured Data?
The problems faced in storing semi-structured data are:
- Since semi-structured data has an irrational structure, it becomes difficult to interpret the relationships between data.
- Since schema and data are highly dependent on each other, any change in queries changes the schema too.
- The difference between schema and data is very difficult to notice, making it difficult to design the structure of data.
- The semi-structured data is difficult to store; therefore, its storage cost is extremely high.
- The semi-structured data is generated in large volumes, which requires powerful and effective software.
What Are The Solutions For Storing Semi-Structured Data?
Some of the plausible solutions in response to the difficulties are:
- Semi-structured data can be stored in DBMS, which is specially created for it.
- Semi-structured data can be rendered by XML. XML allows the users to alter the attributes, tags, and elements and help store the data in hierarchical form.
- Another way of storing semi-structured data is through Object Exchange Model (OEM).
- RDBMS helps store the semi-structured data by mapping it to the relational schema.
How To Extract Information From Semi-Structured Data?
The semi-structured data lacks a proper structure making it complicated to index the data. Therefore the data can be extracted by:
- Using graph-based models such as OEM to index the data.
- OEM uses a data modelling technique that helps store and index the data in the graph-based model. Also, it is relatively easier to find the data in the model
- XML stores the data in a hierarchical form which allows it to be indexed.
- Various mining tools can also be used to index the data.
Difference Between Structured And Semi-structured Data
Some of the top-notch differences between the structured and semi-structured data are:
Structured data is based on relational database tables, whereas semi-structured data is based on XML/RDF (Resource Description Framework)
2. Transaction Management
Structured data comprises matured transactions and multiple concurrency techniques. Semi-structured data does not contain mature data but is derived from DBMS.
3. Version Management
Versioning over rows and tables is possible in structured data. Versioning over graphs and tables is possible in semi-structured data.
Structured data has a rigid schema and depends on it. The semi-structured data has a less dependent schema and is highly flexible.
Scaling structured data is very complex. Scaling semi-structured data is easy.
Structured data is very robust, whereas semi-structured data is not very robust.
Structured data allows the complex joining of queries. Semi-structured data comprises queries from anonymous modes.
Structured data can be easily organized, whereas semi-structured lacks structure making it difficult to organize it.
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Examples Of Semi-Structured Data
Some of the top-notch examples of semi-structured data are:
When you take a picture with your mobile phone, the image is stored by its timestamp, date, and information in the gallery. Afterwards, you can rename the image or categorize images into a separate group.
Emails comprise structured information regarding sender, recipient, subject, and date, which are automatically classified into Inbox, Spam, or Outbox. The data within the emails is unstructured and can be searched via keywords.
Social Media Platforms
Facebook organizes data into groups, pages, or Marketplace but the comments, content, and likes are semi-structured. Similarly, tweets on Twitter and images/videos on Instagram, Pinterest, and YouTube are semi-structured data.
Machine Generated Semi-structured data
Sensory data like weather updates, forecasts, traffic conditions, satellite imagery, and video footage are examples of semi-structured data.
Electronic Data Interchange (EDI)
EDI is an electronic transmission of business documents that were previously transmitted via papers such as invoices or purchase orders. EDI uses multiple standard formats such as ANSI, EDIFACT, TRADACOMS, and ebXML. For a business to use EDI, they must use the standard format.
EDI allows efficient transmission and cost-effective solutions. The data within EDI is unstructured.
NoSQL (not only structured query language) refers to non-relational databases which are used to store both structured and unstructured data. NoSQL is ideal for unstructured data as it has high scalability and makes it easier to search unstructured data.
What Is The Best Example Of Semi-Structured Data?
The best example of semi-structured data emails. A business email addressed to customers comprises specific details like time, date, product details, file size, etc., which are recognized by the algorithm. However, specific details like changing product names and specifications might not be recognized by the algorithm.
How To Analyze Semi-Structured Data?
Before the advent of machine learning techniques, analyzing semi-structured data was a bit complicated as people had to search and sort the data manually. The AI-guided machine learning technology can effectively break down and analyze semi-structured data within seconds.
There are various techniques available now that can easily analyze semi-structured data. For example, a topic analysis is a machine learning technique that efficiently scans and reads through thousands of documents, emails, social media posts, etc., and categorizes them by topic, date, or subject.
Another technique, sentiment analysis, allows you to scan the documents and analyze them for opinion polarity such as positive, negative, or neutral.
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Is Excel Semi-Structured Data?
Excel is a structured data platform as the data is sorted in predefined cells in rows and columns that are recognized by the algorithm. Since structured data depends on the data model therefore excel is a structured platform.
What Is Unstructured Data Example?
Unstructured data is a type of data that does not follow a structural sequence and is not sorted into rows and columns. Examples of unstructured data include video, audio files, images, or social media posts.
Is CSV Structured Or Semi-Structured?
CSV is a semi-structured text file that contains hierarchical tables and does not have the same level of organization as structured data.
Who Uses Semi-Structured Data?
Many businesses use semi-structured data for various purposes. For example, a restaurant business may ask its customers for online reviews. The content within the reviews is unstructured data, whereas the number of customers posting the reviews is structured data. Combining the numerical data and content gives the companies semi-structured data, which they can use to gain in-depth knowledge.
Where To Store Semi-Structured Data?
Semi-structured data can be stored via:
Database management system
DBMS helps you to analyze, store, transfer, and modify data. There is a special DBMS software designed to manage the semi-structured data.
Relational Database Management System
RDBMS is a type of DBMS that stores data in tabular form.
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Is PDF A Type Of Semi-Structured Data?
PDF is a type of semi-structured data as it is an image. The content in it might be unstructured, but since pdf is an image it contains structured information such as date, timestamp, or usernames which makes pdf files semi-structured.
Are Social Media Platforms Structured Or Unstructured?
Social media platforms comprise posts and pictures/videos that are uploaded by users making it difficult for computers to decipher them. Social media platforms assign metadata to each user's respective post, which contains the information regarding that post rendering it readable by computers.
What Is Structured Data?
Structured data is a type of Big Data that has a predefined format and follows an organizational structure. Structured data is quantitative data that fits the rows and columns of the relational database and spreadsheets. For example, credit card numbers, dates, addresses, geolocation, etc.
Structured data is easily read by machines and rapidly understood by people working with the relational database management system. The language used to manage structured data is known as
Structured Query Language or SQL. SQL was developed by IBM in the 1970s, which is helpful for handling relationships of the data within databases.
Advantages Of Structured Data
Some of the top-notch advantages of structured data are:
The best advantage of structured data is that it is easily recognized by machines and algorithms. The organized nature of structured data makes it easier to analyze and manage queries.
Structured data can be easily understood and used by businesses. They don't need to have an in-depth understanding and knowledge regarding the different relationships of the data.
Since structured data has been around for years, there are virtually many different platforms and tools that can analyze and access structured data.
Disadvantages Of Structured Data
Some of the disadvantages of structured data are:
Since the structured data has a predefined and organized format, it becomes difficult to use the data on various occasions limiting its flexibility.
Structured data is stored in data warehouses. Any change in the data will update all of the structured data. This takes time, cost, and resources to make amends.
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What Is Unstructured Data?
Unstructured data is a type of qualitative Big Data that does not follow a structural pattern or has any organization. Managing and analyzing unstructured data is a bit difficult with the traditional machine learning methods.
For example, audio files, activity, social media posts and satellite imagery, etc., are types of unstructured data. Unstructured data is managed by the non-relational search query language NoSQL Database.
Advantages Of Unstructured Data
Some of the advantages of unstructured data are:
Unstructured data can be easily collected and managed as compared to structured or semi-structured data.
Data Lake Storage
Unstructured data can be stored in cloud data lakes which enables massive storage options. Cloud data lakes are cost-effective as they provide pay per use method.
Disadvantages Of Unstructured Data
Some of the disadvantages of unstructured data are:
The most significant disadvantage of unstructured data is that an average business user cannot understand or analyze unstructured data. This is because unstructured data does not follow a set pattern. An expert data scientist can manage unstructured data.
In addition to expertise, unstructured data requires specialized tools designed specifically for unstructured data. These tools are limited in variety, so the users have limited options to consider.
Difference Between Structured And Unstructured Data
Structured data can be managed by business owners. Unstructured data is managed by a data scientist.
Structured data has schema on- write. Unstructured data has schema on-read.
Structured or quantified data is commonly stored in data warehouses. Unstructured data is stored on cloud data lakes.
Structured data has a predefined format. Unstructured data has a native format.
Structured data has select data types. Unstructured data has many conglomerated types.
Structured data is quantitative data that comprises numbers and values. Unstructured data is qualitative data, which includes sensors, audio, and video.
Structured data is used in machine learning. Unstructured data is used in data mining and natural language processing.
Structured data is sourced from web servers, logs, online forms, etc. Unstructured data is sourced from emails, messages, or word documents.
Structured data requires less storage space. Unstructured data requires more storage space.
Structured data is highly scalable. Unstructured data is less scalable.
Semi-structured data has a litany of benefits for the business if one tries to understand it. It may lack structure and organization but provides valuable customer feedback and insights. Companies can use semi-structured data to track their customers’ reviews, engagement and online behaviour.