PapersFlow Research Brief
Big Data Technologies and Applications
Research Guide
What is Big Data Technologies and Applications?
Big Data Technologies and Applications is a field that examines technologies for managing, analyzing, and applying large-scale data volumes alongside their societal, industrial, and economic effects in the digital age.
The field encompasses 26,596 works focused on data management, machine learning, ethical considerations, and industry transformations. Key areas include data analytics, artificial intelligence, and social impacts as highlighted in highly cited papers. Growth data over the past five years is not available in the provided records.
Topic Hierarchy
Research Sub-Topics
Big Data Analytics Techniques
This sub-topic covers scalable algorithms for processing large datasets, including Hadoop, Spark, and real-time analytics. Researchers develop methods for pattern discovery and predictive modeling.
Ethical Issues in Big Data
This sub-topic addresses privacy, bias, and surveillance concerns in big data practices. Researchers propose frameworks for ethical data governance and algorithmic fairness.
Big Data in Machine Learning
This sub-topic explores training deep learning models on massive datasets, tackling scalability and overfitting. Researchers focus on distributed computing and transfer learning.
Big Data Applications in Industry
This sub-topic examines sector-specific uses, such as predictive maintenance in manufacturing and personalization in retail. Researchers evaluate ROI and implementation challenges.
Geospatial Big Data Analysis
This sub-topic leverages platforms like Google Earth Engine for planetary-scale analysis of satellite and sensor data. Researchers study environmental monitoring and urban planning applications.
Why It Matters
Big data technologies enable planetary-scale geospatial analysis through platforms like Google Earth Engine, which processes petabytes of satellite imagery spanning over 30 years for climate and land surface monitoring, as shown by Gorelick et al. (2017) with 12,899 citations. In industry, these technologies support surveillance capitalism, where companies like Google accumulate data for behavioral prediction, impacting information civilization according to Zuboff (2015) with 2,759 citations. Gandomi and Haider (2014) outline methods beyond mere size, aiding analytics in business with 4,010 citations, while boyd and Crawford (2012) address critical ethical questions in data access across disciplines, cited 5,188 times.
Reading Guide
Where to Start
"Beyond the hype: Big data concepts, methods, and analytics" by Gandomi and Haider (2014) provides a foundational broader definition of big data beyond size, making it ideal for beginners to grasp core concepts and methods with 4,010 citations.
Key Papers Explained
Gorelick et al. (2017) in "Google Earth Engine: Planetary-scale geospatial analysis for everyone" (12,899 citations) applies big data to geospatial platforms, building on foundational questions from boyd and Crawford (2012) in "CRITICAL QUESTIONS FOR BIG DATA" (5,188 citations) about data access ethics. Gandomi and Haider (2014) in "Beyond the hype: Big data concepts, methods, and analytics" (4,010 citations) expands methods, while Zuboff (2015) in "Big other: Surveillance Capitalism and the Prospects of an Information Civilization" (2,759 citations) critiques societal applications. Chen and Zhang (2014) in "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data" (2,968 citations) surveys techniques connecting these works.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints show no new developments in the last six months, and news coverage over the past 12 months is unavailable, indicating a stable current state focused on established applications like geospatial analysis and ethical critiques.
Papers at a Glance
Frequently Asked Questions
What is Google Earth Engine in big data applications?
Google Earth Engine is a platform for planetary-scale geospatial analysis using a catalog of petabytes of satellite images with over 30 years of monitoring data. Gorelick et al. (2017) demonstrate its use for climate and earth surface analysis, accessible to everyone with 12,899 citations. It executes analyses on satellite data without local downloads.
What critical questions arise in big data research?
Boyd and Crawford (2012) identify key questions on access to massive data quantities produced by people, things, and interactions across computer science, physics, economics, and sociology. Their work, with 5,188 citations, highlights diverse scholarly demands in the big data era. Ethical and methodological challenges are central.
How do big data concepts extend beyond size?
Gandomi and Haider (2014) define big data by unique characteristics beyond volume, including velocity and variety, with rapid industry adoption outpacing academic methods. Cited 4,010 times, the paper covers concepts, methods, and analytics. It addresses the hype surrounding big data applications.
What are data-intensive challenges in big data?
Chen and Zhang (2014) survey challenges, techniques, and technologies for data-intensive big data applications, cited 2,968 times. The work covers management of large-scale data volumes. It provides an overview grounded in information sciences.
What is surveillance capitalism in big data contexts?
Zuboff (2015) describes surveillance capitalism as an accumulation logic in networked spheres, using Google as a primary example for behavioral data extraction. Cited 2,759 times, it examines prospects for information civilization. Institutional practices drive these big data applications.
What methods are used for categorical data in big data analytics?
Long and Freese (2014) present regression models for categorical dependent variables using Stata, with 4,667 citations. The book covers model implementation and software use. It supports statistical analysis in big data contexts.
Open Research Questions
- ? How can ethical frameworks address biases in planetary-scale data analysis from sources like satellite imagery?
- ? What methods mitigate privacy risks in surveillance capitalism driven by big data accumulation?
- ? Which analytics techniques best handle the velocity and variety dimensions beyond data volume?
- ? How do interdisciplinary approaches resolve access disparities for massive interaction data across fields?
- ? What error reduction techniques apply to physical sciences data in large-scale big data environments?
Recent Trends
No recent preprints from the last six months or news coverage in the past 12 months are available, maintaining emphasis on established works such as Gorelick et al. with 12,899 citations on geospatial platforms and boyd and Crawford (2012) with 5,188 citations on critical questions.
2017The field holds 26,596 works with no specified five-year growth rate.
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