⏳
DIN: AI Agent Blockchain
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English
  • ABOUT DIN
    • ⏳ Overview
    • 🛣️ Our Journey
  • The Concept
    • 💡 Market and Trend Analysis
      • Overview of the current data trend and market
      • Overview of the current AI trend and market
      • Existing gaps and opportunities in the market
    • 🏠 DIN Architecture
      • 🟡Data Layer: All for the Data
        • Data Flow of AI
        • Data Collection
        • Data Validation
        • Data Vectorization
        • The Reward Mechnism
      • 🟩Service Layer: Toolkit for AI-Agent
        • LLMOps
        • RAG (Retrieval Augmented Generation)
          • Hybrid Search
          • Rerank
          • Retrieval
        • Annotation Reply
      • 💙Application Layer: The Ecosystem and Product
        • Analytix
        • xData
        • Reiki
  • How DIN works
    • ⛓️DIN Blockchain
      • 🌏Mainnet
      • 🧪Testnet
    • 🏤DIN Foundation
      • Team&Advisor wallet
      • MM & Liquidity wallet
      • Community wallet
      • Investors wallet
      • Ecosystem wallet
    • 💰 Tokenomics and Utilities
      • Token Allocations
      • Airdrop
      • Contract Details
      • Use cases for the token within the ecosystem
  • HOW TO JOIN
    • 🧲xData Explained
    • ⚙️Chipper Node Explained
      • How to run Chipper Node
      • Farm xDIN
      • Delegation
        • Revoke delegation
        • As an Operator
      • Node Stats
      • Smart Contract Addresses
    • 🤑Earn $DIN
    • 💹Staking
    • 🌉Buy $DIN
  • ROADMAP
    • 🎆 2025 Forward
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  1. The Concept
  2. 🏠 DIN Architecture
  3. Data Layer: All for the Data

Data Flow of AI

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Data Flow is a machine learning pattern representing the data movement sequence in the AI engineering life cycle.

First, Data is processed layer by layer, as shown in Fig.1, to prepare it for storage, training, etc.

Then, data passes through processing layers as it is stored, refined, and prepared for use in Machine Learning models and applications. In a more functional perspective, the data is then used by different machine learning function groups, as shown below:

A detail for each layer in the above chart is as follows:

Sources

Data sources include:

  • Company Internal Databases

  • Company Internal Files

  • Websites

  • Public Data

  • Smartphone Apps

  • IoT Devices

  • Commercial Data Aggregators

  • Point of Sale

  • Corporate Internal Processes

  • Social Media

  • Data Streams

Capture

Capture mechanisms include:

  • Website Scraping

  • Website and Smartphone Chat Dialogues

  • Website and Smartphone Form Submissions

  • IoT Device Interfaces

  • Commercial Data Aggregator Feeds

  • Corporate Internal Process Feeds

Pipeline

Pipeline processes include:

  • Data Ingestion

  • Data Temporary Storage

  • Data Subscription

  • Data Publication

Databases

Databases include:

  • Data Lakes

  • Sequel Databases

  • Document Databases

  • Graph Databases

ETLs

ETLs Include:

  • Extract Functions: pulling data from selected sources

  • Transform Functions: normalization, regularization, aggregation

  • Load Functions: saving data in formats for use in modeling processes

Models

Model-type category examples include:

  • Artificial Neural Networks

  • Decision Trees

  • Probabilistic Graphical Models

  • Cluster Analysis

  • Gaussian Processes

  • Regression Analysis

Applications

Application examples include:

  • Medical Diagnosis

  • Autonomous Vehicles

  • Chatbot Dialog

  • Image Recognition

  • Face Recognition

  • Product Recommendations

  • Churn Prediction

  • Malware Detection

  • Search Refinement

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Data Flow and Functional Groups in AI