Big data definition in computer science
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Big Data Definition in Computer Science: Key Features and Characteristics
Core Attributes: Volume, Velocity, Variety, and More
In computer science, "big data" is most commonly defined by several key characteristics, often referred to as the "V's": high Volume (large amounts of data), high Velocity (rapid data generation and processing), and high Variety (diverse data types and sources) De Mauro2016Arbia2021Yaseen2020+3 MORE. Some definitions also include additional attributes such as Veracity (data quality and trustworthiness), Value (potential to generate insights), and Complexity (challenges in storage, analysis, and visualization) Arbia2021El-Astal2025Kitchin2016.
Technological and Analytical Requirements
Big data is not just about the size or speed of data, but also about the need for specialized technologies and analytical methods to process, store, and extract value from these massive datasets. Traditional data processing tools are often inadequate, so big data requires advanced computing systems, new data-intensive techniques, and innovative analytical platforms De Mauro2016Yaseen2020Kune2015+1 MORE.
Data Sources and Real-World Impact
Big data is generated from a wide range of sources, including both real-world devices and virtual platforms. The data is collected, stored, and organized algorithmically, enabling computational analysis to uncover patterns, trends, and associations that can inform decision-making across many domains Have2021El-Astal2025. The phenomenon is driven by the digital era, where the volume of data produced is growing exponentially .
Evolving Definitions and Perspectives
There is no single, universally accepted definition of big data in computer science. Instead, definitions vary across academia and industry, reflecting different perspectives and research focuses. However, most agree that big data involves datasets so large, fast, or complex that they require new forms of processing to enable enhanced decision-making, insight discovery, and process optimization Ylijoki2016De Mauro2015Diebold2020.
Ontological and Conceptual Clarity
Recent research highlights that big data is not a monolithic concept but encompasses multiple forms, each with its own combination of defining traits. While volume, velocity, and variety are common markers, other features like exhaustivity (completeness), resolution (granularity), and scalability are also important in distinguishing big data from traditional datasets . Achieving conceptual clarity is essential for effective use and further development of big data technologies Ylijoki2016Kitchin2016.
Conclusion
In summary, big data in computer science refers to information assets characterized by high volume, velocity, and variety, often accompanied by complexity and value potential. These datasets require specialized technologies and analytical methods for storage, processing, and analysis, enabling organizations to extract meaningful insights and drive innovation. While definitions continue to evolve, the core idea remains centered on the challenges and opportunities presented by massive, fast, and diverse data in the digital age De Mauro2016Arbia2021Yaseen2020+3 MORE.
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