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Methods and Applications of Data Management and Analytics

This Special Issue of Applied Sciences focuses on innovative methods and applications in data management and analytics, addressing the challenges posed by the growing volume, velocity, and variety of data in today's world. This Issue highlights advancements in big data processing, data mining, AI-dr...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2025
Fag:
data separability
data complexity
deep learning theory
catapult phase
neural tangent kernel
artificial intelligence
computer vision
data requirements
data-centric artificial intelligence
deep learning
label quality assurance
machine learning
object detection
requirements engineering
AI fairness
bias analysis
data analytics
weather prediction
climate prediction
meteorological forecasting
survey
job recommendation
semantic keyword matching
reciprocity
social networks
college graduates
electroencephalogram
emotion classification
data mining
data analysis
human management
employee performance scores
demand prediction
Graph Neural Network
spatial information
anomaly generation
variational Bayes
attention mechanism
recurrent neural network
industrial control system
privacy
privacy risk
privacy risk assessment
mobility
re-identification
computation improvements
risk
trajectory
carbon pricing
temporal convolutional network
climate policy
time series forecasting
NAND memories
data extraction and analysis
electronic evidence
forensic electronics
evidence acquisition
experimental learning
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online adgang:ONIX_20250812T095121_9783725832064_77
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Summary:This Special Issue of Applied Sciences focuses on innovative methods and applications in data management and analytics, addressing the challenges posed by the growing volume, velocity, and variety of data in today's world. This Issue highlights advancements in big data processing, data mining, AI-driven database systems, graph data analysis, and spatial–temporal data management, among other key areas.

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