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Questions and Answers
Which of the following is an example of semi-structured data?
Which of the following is considered unstructured data?
What is the primary purpose of metadata?
Why is metadata particularly important for unstructured data?
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What is the primary characteristic of structured data?
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What does data collection involve?
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Semi-structured data can best be described as:
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What process allows data to be stored for future use?
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Which method is NOT typically used for analyzing unstructured data?
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An example of structured data would be:
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What is the role of a Database Management System (DBMS)?
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Which of the following represents a key difference between structured and unstructured data?
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Why is processing data essential?
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Which of the following describes a limitation of a pen and paper approach to data?
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What would best describe the format of unstructured data?
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An example of semi-structured data is:
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What is a key disadvantage of file processing systems related to program and data structure changes?
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Which issue arises from different systems having separate copies of data in file processing systems?
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What challenge do programmers face regarding file formats in file processing systems?
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What is a significant result of limited data sharing in file processing systems?
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What is a drawback related to metadata maintenance in file processing systems?
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Which of the following best describes program and data interdependence in file processing systems?
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How does duplication of data affect file processing systems?
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What problem related to language compatibility can arise in file processing systems?
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Study Notes
Data Processing Concepts
- Data is not information: Information is derived from data through processing and interpretation. Understanding is gained from information, and wisdom from understanding.
- Data is a collection of characters, numbers, and symbols: It represents values for situations or variables.
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Examples of data:
- Personal data: Name, age, gender, and contact details.
- Transaction data: Generated from banking, ticketing, shopping, and online or offline interactions.
- Multimedia data: Images, graphics, animations, audio, and video.
- Documents and web pages.
- Online communications: Posts, comments, and messages.
- Sensor data
- Satellite data: Meteorological, communication, and earth observation data.
Data Types by Format
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Structured data: Organized and stored in a well-defined format (typically tabular).
- Each column represents a unique attribute or variable.
- Each row represents an observation with data for all attributes.
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Unstructured data: Data that doesn't conform to predefined formats or structures.
- Examples: Text, images, audio, video, social media posts, emails, documents, and web pages.
- Requires advanced techniques to analyze, like natural language processing, computer vision, and Machine Learning.
- Semi-structured data: Data with some structure, but not fitting neatly into traditional tabular formats. - Examples: XML files, JSON data, and log files.
Metadata
- Metadata describes data: It provides additional information or descriptors about the characteristics of data, aiding in understanding and insights.
Data Collection and Storage
- Data needs to be collected first: Collected from appropriate sources or identified from existing sources.
- Data is continuously generated: From interactions with digital media, hospital data, shopping data, and other sources.
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Stored for future use: Stored on devices for retrieval and processing.
- Examples: Files on computers, databases.
Data Processing
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Process data to get results: Large amounts of data cannot be used to draw conclusions directly, but need to be processed for effective analysis and decision-making.
- Finding information from large datasets can be difficult without processing.
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File Processing System (FPS)
- A collection of programs that provide services for users (e.g., reports).
- Each program manages its own data files.
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Disadvantages of File Processing Systems:
- Program-Data Dependence: Changes in data structure require changes to the program, making it inflexible.
- Metadata Maintenance: Each program maintains its own metadata, increasing complexity.
- Data Redundancy: Duplicated data across systems wastes storage and causes inconsistencies.
- Limited Data Sharing: Difficult to access and share data across programs.
- Lengthy Development Times: Programmers design file formats, increasing development time and effort.
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Description
Test your understanding of data processing concepts, including the distinction between data and information. This quiz covers various types of data, their formats, and examples from personal, transaction, and multimedia contexts.
