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This document serves as a lecture outline and educational resource focused on XML, semistructured data, DTDs, XPath, and related topics; it provides definitions, examples, and additional readings.
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How to fill out XML and Semistructured Data

01
Identify the purpose of your XML or semistructured data document.
02
Determine the structure you need, including elements, attributes, and data types.
03
Begin creating the XML document with a prolog declaration if necessary.
04
Define a root element that encapsulates the entire document.
05
Create child elements within the root element as needed, ensuring proper hierarchy.
06
Add attributes to elements to provide additional metadata if required.
07
Validate your XML against a schema (XSD) if applicable to ensure data integrity.
08
Save your document with a .xml file extension for XML files.

Who needs XML and Semistructured Data?

01
Web developers who need to manage data interchange formats.
02
Organizations that require structured data representation for integration.
03
Data analysts who work with unstructured or semi-structured data sources.
04
Software developers building applications that leverage XML data.
05
Businesses needing to format data for APIs and data exchange protocols.
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HTML code, graphs and tables, e-mails, XML documents are examples of semi-structured data, which are often found in object-oriented databases.
Examples of semi-structured data include JSON and XML are forms of semi-structured data. The reason that this third category exists (between structured and unstructured data) is because semi-structured data is considerably easier to analyse than unstructured data.
To reiterate, the main difference between unstructured and semi-structured data is that unstructured data follows no pre-defined format, while semi-structured data is only partly unstructured.
XML, JSON, Avro, Parquet are all categorized as semi-structured data. The data from sensors and server logs can easily be categorized in this bucket, since it generally appears in or can easily be converted to JSON/CSV-like formats.
Examples of Semi-Structured Data Emails: Emails are a classic example of semi-structured data. They have defined fields like the sender, recipient, subject, and date, but the body of the email is unstructured text. XML, JSON, and CSV files: These file types are commonly used to store and transmit data on the web.
What are the similarities between JSON and XML? JSON and XML are both data serialization formats. They allow you to exchange data across different applications, platforms, or systems in a standardized manner.
Examples of semi-structured data include JSON and XML are forms of semi-structured data. The reason that this third category exists (between structured and unstructured data) is because semi-structured data is considerably easier to analyse than unstructured data.

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XML (eXtensible Markup Language) is a markup language used to store and transport data in a structured format that is both human-readable and machine-readable. Semistructured data refers to data that does not conform to a fixed schema but still has some organizational properties, typically represented in formats like XML or JSON.
Organizations and individuals who need to share or transmit data in a structured format may be required to file XML or semistructured data, especially in scenarios involving regulatory compliance, data exchange or integration between systems.
To fill out XML or semistructured data, one needs to follow the specific schema or structure defined for the data. This typically involves creating an XML document with the necessary tags and attributes that correspond to the data fields outlined in the schema.
The purpose of XML and semistructured data is to facilitate the encoding, exchange, and storage of data in a format that can be easily interpreted by different applications and systems. It allows for the flexibility to adapt to various data types without being constrained by a rigid schema.
The information that must be reported in XML and semistructured data typically includes data fields defined by the schema being used, such as names, dates, identification numbers, and other relevant attributes depending on the specific use case or regulatory requirements.
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