Introduction to the Use Case Structure

Aralia Use Case: SDGs Inclusive Growth

Aralia Use Case: Public Works Bid Management and Risk Alert System

Aralia Use Case: Cabbage Supply and Demand Imbalance Analysis

🎯 1. Problem Statement & Background

• Description of the problem: What social, industrial, or research problem is this use case trying to solve?
• Context Statement: The source and motivation of the problem, such as a policy pain point, business challenge, or environmental trend.
• Stakeholder/User Persona: Who will use this analysis and Agent?
• Possible questions: Leading question, e.g., "Is the air quality in a certain area improving year by year?

📊 2. Data Sources

• Data sources and links
• Data fields and format
• Update frequency
• Is pre-processing performed? How to handle missing values or field merging?

🛠️ 3. Aralia Demo

• User's Operation Workflow:
  • Introduction
  • Create Chart
  • Save as Landmark
• Visualization examples (e.g., time trends, maps, bar graphs, etc.)
• Landmark preview (Chart will be refreshed when data is updated.).

📈 4. Analysis Results and Insights

• Results in Brief
• Insight Highlights
• Graphical illustration of each finding (using the Landmark screen as illustration)
• Recommended Actions

🧠 5. AI Agent Demo

• Sample user questions (e.g., "Has there been a change in an indicator in the last five years?")
• Sample agent response (Screenshot + Explanation)

🔧 6. Agent Setup Tutorial

• Step-by-step instructions for building agent:
  • Agent background
  • Introduction to LLM
  • Tutorial on agent construction
  • How to plug in to the front-end interface (e.g. Chat widget)
• Flowchart / Screenshot
• Common errors and troubleshooting

🧾 7. Conclusion & Next Steps

• Extended applications (e.g. different counties and cities, different indicators)
• Encourage users to try it out themselves, join the development or build a community
• Call to Action (CTA): Invite to open an account, create the first Landmark, build your own agent, etc.