Big Data_DA1_19

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School

University of the People *

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Course

CS 3440

Subject

Geography

Date

May 3, 2024

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Pages

1

Uploaded by MateExploration6855 on coursehero.com

We all certainly agree that big data is becoming a big game-changer in mostly all of modern industries. In your own words, describe what do you understand by the term ‘big data’ and explain its types. Do your own research and provide at least six real-time examples of big data. Big data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. + Volume: Think petabytes, exabytes, even zettabytes of data generated from diverse sources like social media interactions, sensor readings, and financial transactions (Chen, Zhang, & Zhou, 2014). « Variety: This data comes in various formats, ranging from structured relational databases to unstructured text, images, and videos, demanding flexible analytical approaches. + Velocity: The speed at which data is generated and needs to be analyzed is constantly increasing, requiring real-time insights and agile decision-making. Big data can be categorized as structured, semi-structured, or unstructured. Structured data is information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a predetermined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs. Semi-structured data is a form of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Real-time examples of big data include: 1. Personalized Retail: Analyzing customer purchase history, browsing behavior, and social media interactions allows retailers to recommend products, offer targeted promotions, and optimize store layouts for a more personalized shopping experience (Deloitte, 2020). 2. Fraud Detection: Financial institutions leverage big data analytics to identify suspicious transactions, prevent fraud attempts, and protect customer information in real-time (IBM, 2023). 3. Healthcare Research: By analyzing vast datasets of medical records, genomic data, and wearable sensor information, researchers can gain insights into disease patterns, develop personalized treatment plans, and accelerate drug discovery (Genomics England, 2023). 4. Traffic Management: Smart cities utilize big data from traffic sensors, cameras, and public transportation systems to optimize traffic flow, reduce congestion, and improve overall urban mobility (McKinsey & Company, 2020). 5. Weather Forecasting: Combining historical weather data with real-time sensor readings and climate models enables meteorologists to create more accurate weather forecasts and issue timely warnings for extreme weather events (National Oceanic and Atmospheric Administration, 2023). 6. Climate Change Analysis: By analyzing vast datasets of global temperature readings, oceanographic data, and satellite imagery, scientists can monitor climate trends, understand the impacts of climate change, and inform policy decisions (NASA, 2023). References: + Chen, M., Zhang, W., & Zhou, X. (2014). Big data: From concept to value. IEEE Intelligent Systems, 29(4), 16-25. + Deloitte. (2020). The retail industry’s big data opportunity. + IBM. (2023). How big data is transforming fraud detection. + Genomics England. (2023). About the 100,000 Genomes Project. https://www.genomicsengland.co.uk/about-genomics-england/the-100000-genomes-project/ + McKinsey & Company. (2020). Urban mobility vision: The transformative power of connected technologies. + National Oceanic and Atmospheric Administration. (2023). Climate modeling. + NASA. (2023). NASA climate change. https://climate.nasa.gov/ 567 words
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