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Artificial Intelligence & Big Data - Coggle Diagram
Artificial Intelligence & Big Data
Cloud–Fog–Edge–Endpoint Architecture
Four-Layer Smart Architecture
Perception Layer
The perception layer collects real-world data through cameras, sensors and mobile devices.
Network Layer
The network layer transmits and routes data reliably via wired and wireless communication networks.
Platform Layer
The platform layer integrates cloud, fog and edge resources to provide unified data processing and AI services.
Application Layer
The application layer exposes platform capabilities as business applications and public services for end users.
Fog Computing and Edge Computing
Fog computing introduces intermediate nodes between cloud and devices to aggregate and preprocess data closer to the edge.
Edge computing executes computation directly on or near data-generating devices to minimize latency.
Benefits and Limits
Fog and edge architectures can significantly reduce backbone bandwidth consumption and improve real-time responsiveness.
Edge nodes are resource-constrained and heterogeneous, which makes fine-grained resource management necessary.
Edge Intelligence and Resource Management
Task Offloading
Task offloading decides whether a computation is executed on the device, at the edge or in the cloud to balance latency and energy.
Resource Prediction and Scheduling
Resource prediction uses machine learning models to estimate future workloads and capacity needs.
Resource scheduling dynamically allocates CPU, GPU and bandwidth to meet service-level agreements.
Energy Efficiency and Green Computing
Appropriate distribution of tasks between edge and cloud can reduce overall energy consumption and carbon emissions.
Industry Application Landscape
Finance and Business Services
Risk Management
Credit scoring models use customer transaction and behavior data to estimate default probabilities.
Fraud detection systems analyze transaction patterns in real time to identify anomalies and suspicious activities.
Investment and Wealth Management
Robo-advisors generate portfolio recommendations based on client risk preferences and market data.
Quantitative trading strategies use historical and real-time price data to automatically execute trades.
Customer Operations and Marketing
Customer profiling systems describe customer segments using multi-dimensional data to support targeted marketing.
Recommendation engines suggest products by analyzing browsing, purchase and similarity patterns.
Healthcare and Life Sciences
Medical Imaging and Clinical Prediction
Image recognition models automatically detect lesions on X-ray, CT or MRI images.
Clinical prediction models use electronic health records and monitoring data to forecast disease progression and readmission risk.
Precision Medicine and Omics
Omics analytics link genetic or molecular features with phenotypes to support target discovery and drug development.
Personalized treatment plans are designed by combining genomic profiles with clinical data.
Digital Health and m-Health
Mobile health applications continuously collect physiological and behavioral signals via smartphones and wearables.
Digital health platforms employ AI models to remotely monitor chronic diseases and trigger interventions.
AI for Toxicology
Toxicity prediction models estimate the safety of compounds from chemical structures and bioactivity data.
AI-assisted toxicology can reduce reliance on animal experiments and increase the efficiency of risk assessment.
Smart Cities and Public Governance
Intelligent Transportation
Urban traffic control systems optimize signal timings based on real-time traffic flows to mitigate congestion.
Monitoring systems automatically detect accidents, violations and abnormal traffic patterns.
Energy and Environment
Energy management systems use load forecasting and pricing signals to optimize grid operation and storage dispatch.
Environmental monitoring networks track air and water quality and issue early warnings for pollution events.
E-Government and Public Services
Chatbots can provide 24-hour responses to public service inquiries and reduce front-desk workload.
Public safety platforms integrate multi-source data to support policing, emergency response and disaster management.
Manufacturing and Industry 4.0
Predictive Maintenance
Condition monitoring systems analyze sensor data to predict equipment failures and schedule preventive repairs.
Intelligent Quality Inspection
Visual inspection systems automatically detect defects and dimensional deviations on production lines.
Digital Twin Factory
Digital twin models simulate manufacturing processes in virtual space to optimize operations and resource allocation.
Retail, E-commerce and Customer Experience
Recommendation and Search
Supply Chain and Stores
Precision Marketing
Marketing analytics models evaluate campaign effectiveness and predict response rates of different customer segments.
Supply Chain and Stores
Inventory management systems forecast future demand from historical sales and external factors to guide replenishment.
Data Governance and Trust Mechanisms
Data Governance and Quality
Standards and Metadata
Unified data standards and metadata schemas facilitate cross-system data integration and sharing.
Quality Management
Incomplete or erroneous data directly reduce the accuracy and reliability of AI models.
Privacy, Security and Compliance
Privacy Protection
Handling personal data must follow principles such as data minimization and purpose limitation.
Regulation and Oversight
Data protection regulations require organizations to obtain lawful consent and keep audit trails when collecting and using data.
Security Threats
Adversarial examples and model stealing attacks show that AI models themselves need security hardening.
Blockchain and Governance
Distributed Ledger
Distributed ledgers ensure immutability and traceability of records through consensus mechanisms.
Project Governance
Combining blockchain with AI can provide transparent fund flows and responsibility tracking in multi-party projects.
Ecosystem and Skills for AI and Big Data
Layered AI and Big Data Ecosystem
Enablement Layer
The enablement layer consists of chip vendors, cloud providers and edge infrastructure suppliers that offer computing and storage resources for AI and big-data workloads.
Production Layer
The production layer includes platforms and tools that build AI models and data-processing pipelines on top of large-scale, heterogeneous big-data sources.
Application Layer
The application layer is composed of organizations that embed AI and big-data analytics into industry workflows to support decisions and automation.
Platform Concentration and Big-Data Digital Divide
Platform Concentration
A small number of large technology firms concentrate key big-data assets, computing power and AI talent, forming central hubs of AI and big-data capabilities.
Digital Divide
Resource-poor organizations and regions face structural disadvantages in collecting, storing and analyzing big data and in training or deploying AI models.
Skills and Employment in AI and Big Data
New Roles
AI and big-data development creates new occupations such as data scientists, data engineers, MLOps engineers and edge-AI developers.
Skill-Based Taxonomy
Skill-based employment taxonomies describe AI and big-data jobs by clusters of data-processing, modeling and engineering skills and by proficiency levels instead of traditional job titles.
Technology and Engineering System
MLOps and Productionization
Monitoring and Continuous Learning
Online monitoring of latency, accuracy and input distributions helps detect model degradation in time.
Drift detection and periodic retraining keep models effective during long-term operation.
CI/CD and Deployment
Continuous integration and continuous deployment pipelines automate building, testing and releasing model services.
Containerization and orchestration platforms run models consistently across environments and scale them on demand.
Versioning and Experiment Management
Unified version control for code, data and models is essential for reproducible AI projects.
Experiment tracking systems record hyperparameters, metrics and environments to compare multiple runs.
Data Factory Pipeline
Model Training and Validation
Model training uses optimization algorithms to minimize a loss function on training data and learn model parameters.
Cross-validation and test evaluation assess generalization ability and help prevent overfitting.
Feature Engineering
Feature engineering constructs and selects key variables that transform raw data into machine-readable representations.
High-quality features can significantly improve the predictive power and stability of models.
Data Ingestion and Governance
Data ingestion continuously collects raw data through logs, APIs, sensors and applications.
Data governance improves data quality and compliance through cleansing, deduplication, standardization and access control.
Computing and Storage Infrastructure
Cloud Computing and Distributed Training
Cloud computing provides elastic compute and storage resources through IaaS, PaaS and SaaS for AI workloads.
Distributed training accelerates large-scale model learning by splitting data or model parameters across multiple nodes.
Dedicated AI Platforms
GPU or TPU clusters with high-speed storage support the intensive compute and I/O demands of deep learning.
Composable data centers can dynamically combine compute, storage and network resources for diverse AI workloads.
Data Engineering and Pipelines
Stream processing frameworks process high-throughput data streams in real time to enable low-latency analytics.
End–edge–cloud pipelines move and transform data efficiently across different layers of the architecture.
Tools and Platform Ecosystem
Machine Learning Frameworks
Mainstream machine learning frameworks provide high-level APIs and operator libraries to simplify model development.
Big Data Platforms
Big data platforms support distributed storage, parallel computing and fault tolerance for terabyte-scale or petabyte-scale datasets.
Collaboration and Automation Tools
Notebooks, AutoML and visual modeling tools lower the barrier to data science and AI experimentation.
Foundation and Relationships
Big Data
Data Types
Big data includes structured,
semi-structured and unstructured data
sensor streams
video
images
text
logs
tables
Characteristics
The essential value of big data lies in the patterns and insights extracted from it rather than the sheer amount of data.
Big data is commonly described by the characteristics of large volume, high velocity, high variety, veracity and value.
Definition
Big data refers to data collections whose volume, velocity and variety exceed the processing capacity of traditional database tools.
Artificial Intelligence
AI Concept
Artificial intelligence is a set of techniques that enable machines to perceive, learn, reason and make decisions in complex environments.
Machine Learning Paradigms
Machine learning includes supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
Machine learning is a core method of AI that learns mappings between inputs and outputs from data
Deep Learning
Deep learning uses multi-layer neural networks to automatically learn features from raw data and often outperforms traditional algorithms on large-scale datasets.
Coupling Between AI and Big Data
Data–Model–Feedback Loop
Continuous feedback loops allow AI systems to adapt to environmental changes
New behavioral and operational data generated by AI-driven decisions are fed back into models for further training.
AI as the Brain of Big Data
AI transforms big data analytics into predictions, decisions and automated actions that create business value.
AI can automatically detect patterns and relationships in massive datasets more efficiently and accurately than manual analysis.
Big Data as Fuel for AI
Without sufficient data support, complex AI models cannot fully realize their potential.
The performance of AI models strongly depends on the scale, quality and diversity of available data.