{
  "format": "dataset-manifest/1",
  "slug": "augmented-reality-interactive-home-decor-planning-app",
  "version": 1,
  "seed": "augmented-reality-interactive-home-decor-planning-app:1",
  "spec": {
    "format": "dataset/1",
    "slug": "augmented-reality-interactive-home-decor-planning-app",
    "name": "Virtual Home Staging Dataset",
    "tagline": "User session logs from an augmented reality home staging application featuring furniture and decor selections.",
    "description": "Users experiment with home decor items in an augmented reality setting, visualizing their dream spaces. By selecting furniture, wall decorations, and custom textures, they can tailor their ideal rooms while managing their shopping carts and completing purchases. Engaging with a curated selection of popular styles, users often gravitate towards trends, yet the platform allows room for unique projects that feature custom textures and expert consultations, providing a personalized touch to their home staging experience.\n\nThe user_sessions table captures interactions between users and the platform, detailing individual sessions where users browse items, apply filters, and visualize products in their chosen environments. Each row represents a unique user session, providing insights into the duration of user engagement and the specific items explored. This data can be linked to user profiles to analyze preferences and behaviors, enabling businesses to tailor their offerings and marketing strategies effectively.\n\nPracticing with this dataset allows developers to tackle questions such as: How do different user segments engage with various home decor items? What are the peak times for user engagement? Which products receive the most attention, and how does that correlate with sales conversions? Analyzing session data can reveal patterns of interest and help design targeted promotions or personalized recommendations. The synthetic nature of this data means it can be safely used for development and experimentation without concerns about privacy or real-world implications.",
    "domain": "ecommerce",
    "shape": "hierarchical",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "augmented reality",
      "home decor",
      "furniture",
      "user sessions",
      "ecommerce",
      "virtual staging",
      "interior design"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "user_sessions": {
        "description": "This table captures individual user sessions within the augmented reality home decor application. Each row represents a session, including details about the user's selections and preferences.",
        "rows": 500,
        "columns": {
          "session_id": {
            "kind": "pk",
            "description": "Unique identifier for each user session."
          },
          "user_id": {
            "kind": "uuid",
            "description": "Unique identifier for the user participating in the session."
          },
          "items_selected": {
            "kind": "int",
            "min": 1,
            "max": 15,
            "description": "Number of decor items selected by the user during the session."
          },
          "furniture_selected": {
            "kind": "int",
            "min": 0,
            "max": 15,
            "description": "Number of furniture items selected."
          },
          "wall_decors_selected": {
            "kind": "int",
            "min": 0,
            "max": 15,
            "description": "Number of wall decor items selected."
          },
          "custom_textures_selected": {
            "kind": "int",
            "min": 0,
            "max": 5,
            "description": "Number of custom textures applied by the user.",
            "nullable": true
          },
          "expert_consultation_requested": {
            "kind": "bool",
            "description": "Indicates if the user requested expert consultation during the session.",
            "nullable": true
          },
          "session_duration": {
            "kind": "int",
            "min": 1,
            "max": 120,
            "description": "Duration of the session in minutes.",
            "nullable": true
          },
          "finalized_order": {
            "kind": "bool",
            "description": "Indicates if the user finalized an order during the session."
          },
          "session_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the session took place.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What decor items are most frequently selected by users?",
      "How long do users typically spend in the application during a session?",
      "What percentage of users finalize their orders after a session?",
      "How does the selection of custom textures vary among different users?",
      "Do users who request expert consultations have different selection patterns?"
    ],
    "suggestedQueries": [
      {
        "title": "Count of Items Selected by Users",
        "sql": "SELECT items_selected, COUNT(*) FROM user_sessions GROUP BY items_selected;",
        "dialect": "postgres"
      },
      {
        "title": "Average Session Duration",
        "sql": "SELECT AVG(session_duration) FROM user_sessions;",
        "dialect": "postgres"
      },
      {
        "title": "Finalized Order Rates",
        "sql": "SELECT finalized_order, COUNT(*) FROM user_sessions GROUP BY finalized_order;",
        "dialect": "postgres"
      },
      {
        "title": "Custom Textures vs. Final Orders",
        "sql": "SELECT custom_textures_selected, COUNT(*) FROM user_sessions WHERE finalized_order = TRUE GROUP BY custom_textures_selected;",
        "dialect": "postgres"
      },
      {
        "title": "Consultation Request Patterns",
        "sql": "SELECT expert_consultation_requested, AVG(items_selected) FROM user_sessions GROUP BY expert_consultation_requested;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Ecommerce Product Analysis",
        "body": "Ecommerce analysts can use this dataset to understand user preferences in home decor. By examining which items are frequently selected and how long users engage with the application, businesses can tailor their marketing strategies and inventory to better meet customer needs."
      },
      {
        "title": "Teaching SQL Concepts",
        "body": "Instructors can utilize this dataset to teach SQL querying techniques. With a variety of columns and data types, students can practice writing queries to extract meaningful insights, such as user behavior patterns and selection trends in a practical context."
      },
      {
        "title": "User Experience Research",
        "body": "User experience researchers can analyze session durations and finalize order rates to assess the effectiveness of the application. Insights gained from this dataset can guide improvements in user interface design and overall user engagement strategies."
      },
      {
        "title": "Market Trend Analysis",
        "body": "Market analysts can leverage this dataset to identify emerging trends in home decor preferences. By analyzing the frequency of selected items and the request for expert consultations, they can provide valuable insights to manufacturers and retailers."
      }
    ]
  },
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